2025
Aline Xavier Fidencio; Ioannis Iossifidis
Error-Related Potentials and Reinforcement Learning for Neuroadaptive Systems Proceedings Article
In: BC25 : Bernstein Conference 2025,, BCCN Bernstein Network Computational Neuroscience & Neurotechnology, 2025.
Abstract | Links | BibTeX | Tags: BCI, Machine Learning
@inproceedings{xavierfidencioErrorrelatedPotentialsReinforcement2025,
title = {Error-Related Potentials and Reinforcement Learning for Neuroadaptive Systems},
author = {Aline Xavier Fidencio and Ioannis Iossifidis},
url = {https://abstracts.g-node.org/conference/BC25/abstracts#/uuid/84a56344-585c-4d7c-992d-1f34489770f5},
year = {2025},
date = {2025-10-01},
urldate = {2025-10-01},
booktitle = {BC25 : Bernstein Conference 2025,},
publisher = {BCCN Bernstein Network Computational Neuroscience & Neurotechnology},
abstract = {Error-related potentials (ErrPs) represent the neural signature of error processing in the brain and numerous studies have demonstrated their reliable detection using non-invasive techniques such as electroencephalography (EEG). Over recent decades, the brain-computer interface (BCI) community has shown growing interest in leveraging these intrinsic feedback signals to enhance system performance. However, the effective use of ErrPs in a closed-loop setup crucially depends on accurate single-trial detection, which is typically achieved using a subject-specific classifier (or decoder) trained on samples recorded during extensive calibration sessions before the BCI system can be deployed. In our research, we explore the potential of simulated EEG data for training a truly generic ErrP classifier. Utilizing the SEREEGA simulator, we demonstrate that EEG data can be generated in a cost-effective manner, allowing for controlled and systematic variations in data distribution to accommodate uncertainties in ErrP generation. A classifier trained solely on the generated data exhibits promising generalization capabilities across different datasets and performs comparably to a leave-one-subject-out approach trained on real data (Xavier Fidêncio et al., 2024). In our experiments, we deliberately provoked ErrPs when the BCI misinterpreted the user's intention, resulting in incorrect actions. Subjects engaged in a game controlled via keyboard and/or motor imagery (imagining hand movements), with EEG data recorded using various EEG systems for comparison. Considering the challenges in obtaining clear ErrP signals for all subjects and the limitations identified in existing literature (Xavier Fidêncio et al., 2022), we hypothesize whether a measurable error signal is consistently generated at the scalp level when subjects encounter erroneous conditions, and how this influences closed-loop setups that incorporate ErrPs for improved BCI performance. To address these questions, we assess the effects of the occurrence-to-detection ratio of ErrPs in the classification pipeline using simulated data and explore the impact of error misclassification rates in an ErrP-based learning framework, which employs reinforcement learning to enhance BCI performance.},
keywords = {BCI, Machine Learning},
pubstate = {published},
tppubtype = {inproceedings}
}
Error-related potentials (ErrPs) represent the neural signature of error processing in the brain and numerous studies have demonstrated their reliable detection using non-invasive techniques such as electroencephalography (EEG). Over recent decades, the brain-computer interface (BCI) community has shown growing interest in leveraging these intrinsic feedback signals to enhance system performance. However, the effective use of ErrPs in a closed-loop setup crucially depends on accurate single-trial detection, which is typically achieved using a subject-specific classifier (or decoder) trained on samples recorded during extensive calibration sessions before the BCI system can be deployed. In our research, we explore the potential of simulated EEG data for training a truly generic ErrP classifier. Utilizing the SEREEGA simulator, we demonstrate that EEG data can be generated in a cost-effective manner, allowing for controlled and systematic variations in data distribution to accommodate uncertainties in ErrP generation. A classifier trained solely on the generated data exhibits promising generalization capabilities across different datasets and performs comparably to a leave-one-subject-out approach trained on real data (Xavier Fidêncio et al., 2024). In our experiments, we deliberately provoked ErrPs when the BCI misinterpreted the user's intention, resulting in incorrect actions. Subjects engaged in a game controlled via keyboard and/or motor imagery (imagining hand movements), with EEG data recorded using various EEG systems for comparison. Considering the challenges in obtaining clear ErrP signals for all subjects and the limitations identified in existing literature (Xavier Fidêncio et al., 2022), we hypothesize whether a measurable error signal is consistently generated at the scalp level when subjects encounter erroneous conditions, and how this influences closed-loop setups that incorporate ErrPs for improved BCI performance. To address these questions, we assess the effects of the occurrence-to-detection ratio of ErrPs in the classification pipeline using simulated data and explore the impact of error misclassification rates in an ErrP-based learning framework, which employs reinforcement learning to enhance BCI performance.
Stephan Johann Lehmler; Muhammad Saif Rehman; Ioannis Iossifidis
Activation-Based Indicators of Memorization in ReLU Artificial Neural Networks: A Computational Perspective Proceedings Article
In: BC25 : Bernstein Conference 2025, BCCN Bernstein Network Computational Neuroscience & Neurotechnology, 2025.
Abstract | Links | BibTeX | Tags: Machine Learning
@inproceedings{lehmlerActivationBasedIndicatorsMemorization2025,
title = {Activation-Based Indicators of Memorization in ReLU Artificial Neural Networks: A Computational Perspective},
author = {Stephan Johann Lehmler and Muhammad Saif Rehman and Ioannis Iossifidis},
url = {https://abstracts.g-node.org/conference/BC25/abstracts#/uuid/cfc1121c-9d2c-4017-a781-15e9ec615053},
year = {2025},
date = {2025-09-30},
urldate = {2025-09-30},
booktitle = {BC25 : Bernstein Conference 2025},
publisher = {BCCN Bernstein Network Computational Neuroscience & Neurotechnology},
abstract = {Our recent work presents a stochastic process model of the activations within an ANN and shows a promising indicator to distinguish memorizing from generalizing ANNs. The average λ, or mean firing rate (MFR), of a hidden layer, shows stable differences between memorizing and generalizing networks, comparatively independent of the underlying data used for evaluation. We first show the performance of this indicator during training on benchmark computer vision datasets such as MNIST and CIFAR-10. In a second step, we extend the work to the real-life use case of calibrating a pre-trained model to a new user. We focus on decoding surface electromyographic (sEMG) signals, which are highly variable within and between users, and therefore necessitate frequent user calibration. Especially in situations when user calibration has to only rely on a small number of samples, degradation in performance overtime due to memorization and overfitting is a not unlikely outcome. In those cases, traditional regularization methods that function by observing the performance on a validation set, such as early stopping, don’t necessarily work, because they are evaluated on data from the same subject and set of movements, which features are being memorized. Our new indicators of memorization could help as stable indicators for model performance and give live insights during model calibration when more samples from the new users would be necessary. We evaluate the usefulness of the MFR-indicator for identifying the moment a pre-trained sEMG decoder starts to memorize given inputs},
keywords = {Machine Learning},
pubstate = {published},
tppubtype = {inproceedings}
}
Our recent work presents a stochastic process model of the activations within an ANN and shows a promising indicator to distinguish memorizing from generalizing ANNs. The average λ, or mean firing rate (MFR), of a hidden layer, shows stable differences between memorizing and generalizing networks, comparatively independent of the underlying data used for evaluation. We first show the performance of this indicator during training on benchmark computer vision datasets such as MNIST and CIFAR-10. In a second step, we extend the work to the real-life use case of calibrating a pre-trained model to a new user. We focus on decoding surface electromyographic (sEMG) signals, which are highly variable within and between users, and therefore necessitate frequent user calibration. Especially in situations when user calibration has to only rely on a small number of samples, degradation in performance overtime due to memorization and overfitting is a not unlikely outcome. In those cases, traditional regularization methods that function by observing the performance on a validation set, such as early stopping, don’t necessarily work, because they are evaluated on data from the same subject and set of movements, which features are being memorized. Our new indicators of memorization could help as stable indicators for model performance and give live insights during model calibration when more samples from the new users would be necessary. We evaluate the usefulness of the MFR-indicator for identifying the moment a pre-trained sEMG decoder starts to memorize given inputs
Aline Xavier Fidêncio; Felix Grün; Christian Klaes; Ioannis Iossifidis
Hybrid Brain-Computer Interface Using Error-Related Potential and Reinforcement Learning Journal Article
In: Frontiers in Human Neuroscience, vol. 19, 2025, ISSN: 1662-5161.
Abstract | Links | BibTeX | Tags: adaptive brain-computer interface, BCI, EEG, error-related potentials (ErrPs), Machine Learning, motor imagery (MI), reinforcement learning (RL)
@article{xavierfidencioHybridBraincomputerInterface2025,
title = {Hybrid Brain-Computer Interface Using Error-Related Potential and Reinforcement Learning},
author = {Aline Xavier Fidêncio and Felix Grün and Christian Klaes and Ioannis Iossifidis},
editor = {Frontiers},
url = {https://www.frontiersin.org/journals/human-neuroscience/articles/10.3389/fnhum.2025.1569411/full},
doi = {10.3389/fnhum.2025.1569411},
issn = {1662-5161},
year = {2025},
date = {2025-06-04},
urldate = {2025-06-04},
journal = {Frontiers in Human Neuroscience},
volume = {19},
publisher = {Frontiers},
abstract = {Brain-computer interfaces (BCIs) offer alternative communication methods for individuals with motor disabilities, aiming to improve their quality of life through external device control. However, non-invasive BCIs using electroencephalography (EEG) often suffer from performance limitations due to non-stationarities arising from changes in mental state or device characteristics. Addressing these challenges motivates the development of adaptive systems capable of real-time adjustment. This study investigates a novel approach for creating an adaptive, error-related potential (ErrP)-based BCI using reinforcement learning (RL) to dynamically adapt to EEG signal variations. The framework was validated through experiments on a publicly available motor imagery dataset and a novel fast-paced protocol designed to enhance user engagement. Results showed that RL agents effectively learned control policies from user interactions, maintaining robust performance across datasets. However, findings from the game-based protocol revealed that fast-paced motor imagery tasks were ineffective for most participants, highlighting critical challenges in real-time BCI task design. Overall, the results demonstrate the potential of RL for enhancing BCI adaptability while identifying practical constraints in task complexity and user responsiveness.},
keywords = {adaptive brain-computer interface, BCI, EEG, error-related potentials (ErrPs), Machine Learning, motor imagery (MI), reinforcement learning (RL)},
pubstate = {published},
tppubtype = {article}
}
Brain-computer interfaces (BCIs) offer alternative communication methods for individuals with motor disabilities, aiming to improve their quality of life through external device control. However, non-invasive BCIs using electroencephalography (EEG) often suffer from performance limitations due to non-stationarities arising from changes in mental state or device characteristics. Addressing these challenges motivates the development of adaptive systems capable of real-time adjustment. This study investigates a novel approach for creating an adaptive, error-related potential (ErrP)-based BCI using reinforcement learning (RL) to dynamically adapt to EEG signal variations. The framework was validated through experiments on a publicly available motor imagery dataset and a novel fast-paced protocol designed to enhance user engagement. Results showed that RL agents effectively learned control policies from user interactions, maintaining robust performance across datasets. However, findings from the game-based protocol revealed that fast-paced motor imagery tasks were ineffective for most participants, highlighting critical challenges in real-time BCI task design. Overall, the results demonstrate the potential of RL for enhancing BCI adaptability while identifying practical constraints in task complexity and user responsiveness.
2024
Aline Xavier Fidencio; Christian Klaes; Ioannis Iossifidis
Adaptive Brain-Computer Interfaces Based on Error-Related Potentials and Reinforcement Learning Proceedings Article
In: BC24 : Computational Neuroscience & Neurotechnology Bernstein Conference 2024, BCCN Bernstein Network Computational Networkvphantom, 2024.
Abstract | Links | BibTeX | Tags: BCI, Machine Learning
@inproceedings{AdaptiveBraincomputerInterfaces2024,
title = {Adaptive Brain-Computer Interfaces Based on Error-Related Potentials and Reinforcement Learning},
author = {Aline Xavier Fidencio and Christian Klaes and Ioannis Iossifidis},
url = {https://abstracts.g-node.org/conference/BC24/abstracts#/uuid/03d3dd16-4c50-43d8-b878-abcfa7857386},
year = {2024},
date = {2024-09-18},
urldate = {2024-09-24},
booktitle = {BC24 : Computational Neuroscience & Neurotechnology Bernstein Conference 2024},
publisher = {BCCN Bernstein Network Computational Networkvphantom},
abstract = {Error-related potentials (ErrPs) represent the neural signature of error processing in the brain and numerous studies have demonstrated their reliable detection using non-invasive techniques such as electroencephalography (EEG). Over recent decades, the brain-computer interface (BCI) community has shown growing interest in leveraging these intrinsic feedback signals to enhance system performance. However, the effective use of ErrPs in a closed-loop setup crucially depends on accurate single-trial detection, which is typically achieved using a subject-specific classifier (or decoder) trained on samples recorded during extensive calibration sessions before the BCI system can be deployed. In our research, we explore the potential of simulated EEG data for training a truly generic ErrP classifier. Utilizing the SEREEGA simulator, we demonstrate that EEG data can be generated in a cost-effective manner, allowing for controlled and systematic variations in data distribution to accommodate uncertainties in ErrP generation. A classifier trained solely on the generated data exhibits promising generalization capabilities across different datasets and performs comparably to a leave-one-subject-out approach trained on real data (Xavier Fidêncio et al., 2024). In our experiments, we deliberately provoked ErrPs when the BCI misinterpreted the user's intention, resulting in incorrect actions. Subjects engaged in a game controlled via keyboard and/or motor imagery (imagining hand movements), with EEG data recorded using various EEG systems for comparison. Considering the challenges in obtaining clear ErrP signals for all subjects and the limitations identified in existing literature (Xavier Fidêncio et al., 2022), we hypothesize whether a measurable error signal is consistently generated at the scalp level when subjects encounter erroneous conditions, and how this influences closed-loop setups that incorporate ErrPs for improved BCI performance. To address these questions, we assess the effects of the occurrence-to-detection ratio of ErrPs in the classification pipeline using simulated data and explore the impact of error misclassification rates in an ErrP-based learning framework, which employs reinforcement learning to enhance BCI performance.},
keywords = {BCI, Machine Learning},
pubstate = {published},
tppubtype = {inproceedings}
}
Error-related potentials (ErrPs) represent the neural signature of error processing in the brain and numerous studies have demonstrated their reliable detection using non-invasive techniques such as electroencephalography (EEG). Over recent decades, the brain-computer interface (BCI) community has shown growing interest in leveraging these intrinsic feedback signals to enhance system performance. However, the effective use of ErrPs in a closed-loop setup crucially depends on accurate single-trial detection, which is typically achieved using a subject-specific classifier (or decoder) trained on samples recorded during extensive calibration sessions before the BCI system can be deployed. In our research, we explore the potential of simulated EEG data for training a truly generic ErrP classifier. Utilizing the SEREEGA simulator, we demonstrate that EEG data can be generated in a cost-effective manner, allowing for controlled and systematic variations in data distribution to accommodate uncertainties in ErrP generation. A classifier trained solely on the generated data exhibits promising generalization capabilities across different datasets and performs comparably to a leave-one-subject-out approach trained on real data (Xavier Fidêncio et al., 2024). In our experiments, we deliberately provoked ErrPs when the BCI misinterpreted the user's intention, resulting in incorrect actions. Subjects engaged in a game controlled via keyboard and/or motor imagery (imagining hand movements), with EEG data recorded using various EEG systems for comparison. Considering the challenges in obtaining clear ErrP signals for all subjects and the limitations identified in existing literature (Xavier Fidêncio et al., 2022), we hypothesize whether a measurable error signal is consistently generated at the scalp level when subjects encounter erroneous conditions, and how this influences closed-loop setups that incorporate ErrPs for improved BCI performance. To address these questions, we assess the effects of the occurrence-to-detection ratio of ErrPs in the classification pipeline using simulated data and explore the impact of error misclassification rates in an ErrP-based learning framework, which employs reinforcement learning to enhance BCI performance.
Felix Grün; Ioannis Iossifidis
Controversial Opinions on Model Based and Model Free Reinforcement Learning in the Brain Proceedings Article
In: BCCN Bernstein Network Computational Networkvphantom, 2024.
Abstract | Links | BibTeX | Tags: Machine Learning, Reinforcement learning
@inproceedings{ControversialOpinionsModel2024,
title = {Controversial Opinions on Model Based and Model Free Reinforcement Learning in the Brain},
author = {Felix Grün and Ioannis Iossifidis},
url = {https://abstracts.g-node.org/conference/BC24/abstracts#/uuid/18e92e07-e4b1-43af-b2ac-ea282f4e81e7},
year = {2024},
date = {2024-09-18},
urldate = {2024-09-24},
publisher = {BCCN Bernstein Network Computational Networkvphantom},
abstract = {Dopaminergic Reward Prediction Errors (RPEs) are a key motivation and inspiration for model free, temporal difference reinforcement learning methods. Originally, the correlation of RPEs with model free temporal difference errors was seen as a strong indicator for model free reinforcement learning in brains. The standard view was that model free learning is the norm and more computationally expensive model based decision-making is only used when it leads to outcomes that are good enough to justify the additional effort. Nowadays, the landscape of opinions, models and experimental evidence, both electrophysiological and behavioral, paints a more complex picture, including but not limited to mechanisms of arbitration between the two systems. Model based learning or hybrid models better capture experimental behavioral data, and model based signatures are found in RPEs that were previously thought to be model free or hybrid [1]. The evidence for clearly model free learning is scarce [2]. On the other hand, multiple approaches show how model based behavior and RPEs can be produced with fundamentally model free reinforcement learning methods [3, 4, 5]. We point out findings that seem to contradict each other, others that complement each other, speculate which ideas are compatible with each other and give our opinions on ways forward, towards understanding if and how model based and model free learning from rewards coexist and interact in the brain.},
keywords = {Machine Learning, Reinforcement learning},
pubstate = {published},
tppubtype = {inproceedings}
}
Dopaminergic Reward Prediction Errors (RPEs) are a key motivation and inspiration for model free, temporal difference reinforcement learning methods. Originally, the correlation of RPEs with model free temporal difference errors was seen as a strong indicator for model free reinforcement learning in brains. The standard view was that model free learning is the norm and more computationally expensive model based decision-making is only used when it leads to outcomes that are good enough to justify the additional effort. Nowadays, the landscape of opinions, models and experimental evidence, both electrophysiological and behavioral, paints a more complex picture, including but not limited to mechanisms of arbitration between the two systems. Model based learning or hybrid models better capture experimental behavioral data, and model based signatures are found in RPEs that were previously thought to be model free or hybrid [1]. The evidence for clearly model free learning is scarce [2]. On the other hand, multiple approaches show how model based behavior and RPEs can be produced with fundamentally model free reinforcement learning methods [3, 4, 5]. We point out findings that seem to contradict each other, others that complement each other, speculate which ideas are compatible with each other and give our opinions on ways forward, towards understanding if and how model based and model free learning from rewards coexist and interact in the brain.
Marie Dominique Schmidt; Ioannis Iossifidis
Decoding Upper Limb Movements Proceedings Article
In: BCCN Bernstein Network Computational Networkvphantom, 2024.
Abstract | Links | BibTeX | Tags: BCI, Machine Learning, Muscle activity
@inproceedings{DecodingUpperLimb2024,
title = {Decoding Upper Limb Movements},
author = {Marie Dominique Schmidt and Ioannis Iossifidis},
url = {https://abstracts.g-node.org/conference/BC24/abstracts#/uuid/4725140f-ce7c-4ac5-b694-c627ceeb8d98},
year = {2024},
date = {2024-09-18},
urldate = {2024-09-24},
publisher = {BCCN Bernstein Network Computational Networkvphantom},
abstract = {The upper limbs are essential for performing everyday tasks that require a wide range of motion and precise coordination. Planning and timing are crucial to achieve coordinated movement. Sensory information about the target and current body state is critical, as is the integration of prior experience represented by prelearned inverse dynamics that generate the associated muscle activity. We propose a generative model that uses a recurrent neural network to predict upper limb muscle activity during various simple and complex everyday movements. By identifying movement primitives within the signal, our model enables the decomposition of these movements into a fundamental set, facilitating the reconstruction of muscle activity patterns. Our approach has implications for the fundamental understanding of movement control and the rehabilitation of neuromuscular disorders with myoelectric prosthetics and functional electrical stimulation.},
keywords = {BCI, Machine Learning, Muscle activity},
pubstate = {published},
tppubtype = {inproceedings}
}
The upper limbs are essential for performing everyday tasks that require a wide range of motion and precise coordination. Planning and timing are crucial to achieve coordinated movement. Sensory information about the target and current body state is critical, as is the integration of prior experience represented by prelearned inverse dynamics that generate the associated muscle activity. We propose a generative model that uses a recurrent neural network to predict upper limb muscle activity during various simple and complex everyday movements. By identifying movement primitives within the signal, our model enables the decomposition of these movements into a fundamental set, facilitating the reconstruction of muscle activity patterns. Our approach has implications for the fundamental understanding of movement control and the rehabilitation of neuromuscular disorders with myoelectric prosthetics and functional electrical stimulation.
Stephan Johann Lehmler; Ioannis Iossifidis
Stochastic Process Model Derived Indicators of Overfitting for Deep Architectures: Applicability to Small Sample Recalibration of sEMG Decoders Proceedings Article
In: BC24 : Computational Neuroscience & Neurotechnology Bernstein Conference 2024, BCCN Bernstein Network Computational Networkvphantom, 2024.
Abstract | Links | BibTeX | Tags: Machine Learning
@inproceedings{StochasticProcessModel2024,
title = {Stochastic Process Model Derived Indicators of Overfitting for Deep Architectures: Applicability to Small Sample Recalibration of sEMG Decoders},
author = {Stephan Johann Lehmler and Ioannis Iossifidis},
url = {https://abstracts.g-node.org/conference/BC24/abstracts#/uuid/72f03ff1-61dc-443c-92c2-b623d672ce15},
year = {2024},
date = {2024-09-18},
urldate = {2024-09-24},
booktitle = {BC24 : Computational Neuroscience & Neurotechnology Bernstein Conference 2024},
publisher = {BCCN Bernstein Network Computational Networkvphantom},
abstract = {Our recent work presents a stochastic process model of the activations within an ANN and shows a promising indicator to distinguish memorizing from generalizing ANNs. The average λ, or mean firing rate (MFR), of a hidden layer, shows stable differences between memorizing and generalizing networks, comparatively independent of the underlying data used for evaluation. We first show the performance of this indicator during training on benchmark computer vision datasets such as MNIST and CIFAR-10. In a second step, we extend the work to the real-life use case of calibrating a pre-trained model to a new user. We focus on decoding surface electromyographic (sEMG) signals, which are highly variable within and between users, and therefore necessitate frequent user calibration. Especially in situations when user calibration has to only rely on a small number of samples, degradation in performance overtime due to memorization and overfitting is a not unlikely outcome. In those cases, traditional regularization methods that function by observing the performance on a validation set, such as early stopping, don’t necessarily work, because they are evaluated on data from the same subject and set of movements, which features are being memorized. Our new indicators of memorization could help as stable indicators for model performance and give live insights during model calibration when more samples from the new users would be necessary. We evaluate the usefulness of the MFR-indicator for identifying the moment a pre-trained sEMG decoder starts to memorize given inputs},
keywords = {Machine Learning},
pubstate = {published},
tppubtype = {inproceedings}
}
Our recent work presents a stochastic process model of the activations within an ANN and shows a promising indicator to distinguish memorizing from generalizing ANNs. The average λ, or mean firing rate (MFR), of a hidden layer, shows stable differences between memorizing and generalizing networks, comparatively independent of the underlying data used for evaluation. We first show the performance of this indicator during training on benchmark computer vision datasets such as MNIST and CIFAR-10. In a second step, we extend the work to the real-life use case of calibrating a pre-trained model to a new user. We focus on decoding surface electromyographic (sEMG) signals, which are highly variable within and between users, and therefore necessitate frequent user calibration. Especially in situations when user calibration has to only rely on a small number of samples, degradation in performance overtime due to memorization and overfitting is a not unlikely outcome. In those cases, traditional regularization methods that function by observing the performance on a validation set, such as early stopping, don’t necessarily work, because they are evaluated on data from the same subject and set of movements, which features are being memorized. Our new indicators of memorization could help as stable indicators for model performance and give live insights during model calibration when more samples from the new users would be necessary. We evaluate the usefulness of the MFR-indicator for identifying the moment a pre-trained sEMG decoder starts to memorize given inputs
Aline Xavier Fidêncio; Christian Klaes; Ioannis Iossifidis
A Generic Error-Related Potential Classifier Based on Simulated Subjects Journal Article
In: Frontiers in Human Neuroscience, vol. 18, pp. 1390714, 2024, ISSN: 1662-5161.
Abstract | Links | BibTeX | Tags: adaptive brain-machine (computer) interface, BCI, EEG, Error-related potential (ErrP), ErrP classifier, Generic decoder, Machine Learning, SEREEGA, Simulation
@article{xavierfidencioGenericErrorrelatedPotential2024b,
title = {A Generic Error-Related Potential Classifier Based on Simulated Subjects},
author = {Aline Xavier Fidêncio and Christian Klaes and Ioannis Iossifidis},
editor = {Frontiers Media SA},
url = {https://www.frontiersin.org/journals/human-neuroscience/articles/10.3389/fnhum.2024.1390714/full},
doi = {10.3389/fnhum.2024.1390714},
issn = {1662-5161},
year = {2024},
date = {2024-07-19},
urldate = {2024-07-17},
journal = {Frontiers in Human Neuroscience},
volume = {18},
pages = {1390714},
publisher = {Frontiers},
abstract = {$<$p$>$Error-related potentials (ErrPs) are brain signals known to be generated as a reaction to erroneous events. Several works have shown that not only self-made errors but also mistakes generated by external agents can elicit such event-related potentials. The possibility of reliably measuring ErrPs through non-invasive techniques has increased the interest in the brain-computer interface (BCI) community in using such signals to improve performance, for example, by performing error correction. Extensive calibration sessions are typically necessary to gather sufficient trials for training subject-specific ErrP classifiers. This procedure is not only time-consuming but also boresome for participants. In this paper, we explore the effectiveness of ErrPs in closed-loop systems, emphasizing their dependency on precise single-trial classification. To guarantee the presence of an ErrPs signal in the data we employ and to ensure that the parameters defining ErrPs are systematically varied, we utilize the open-source toolbox SEREEGA for data simulation. We generated training instances and evaluated the performance of the generic classifier on both simulated and real-world datasets, proposing a promising alternative to conventional calibration techniques. Results show that a generic support vector machine classifier reaches balanced accuracies of 72.9%, 62.7%, 71.0%, and 70.8% on each validation dataset. While performing similarly to a leave-one-subject-out approach for error class detection, the proposed classifier shows promising generalization across different datasets and subjects without further adaptation. Moreover, by utilizing SEREEGA, we can systematically adjust parameters to accommodate the variability in the ErrP, facilitating the systematic validation of closed-loop setups. Furthermore, our objective is to develop a universal ErrP classifier that captures the signal's variability, enabling it to determine the presence or absence of an ErrP in real EEG data.$<$/p$>$},
keywords = {adaptive brain-machine (computer) interface, BCI, EEG, Error-related potential (ErrP), ErrP classifier, Generic decoder, Machine Learning, SEREEGA, Simulation},
pubstate = {published},
tppubtype = {article}
}
$<$p$>$Error-related potentials (ErrPs) are brain signals known to be generated as a reaction to erroneous events. Several works have shown that not only self-made errors but also mistakes generated by external agents can elicit such event-related potentials. The possibility of reliably measuring ErrPs through non-invasive techniques has increased the interest in the brain-computer interface (BCI) community in using such signals to improve performance, for example, by performing error correction. Extensive calibration sessions are typically necessary to gather sufficient trials for training subject-specific ErrP classifiers. This procedure is not only time-consuming but also boresome for participants. In this paper, we explore the effectiveness of ErrPs in closed-loop systems, emphasizing their dependency on precise single-trial classification. To guarantee the presence of an ErrPs signal in the data we employ and to ensure that the parameters defining ErrPs are systematically varied, we utilize the open-source toolbox SEREEGA for data simulation. We generated training instances and evaluated the performance of the generic classifier on both simulated and real-world datasets, proposing a promising alternative to conventional calibration techniques. Results show that a generic support vector machine classifier reaches balanced accuracies of 72.9%, 62.7%, 71.0%, and 70.8% on each validation dataset. While performing similarly to a leave-one-subject-out approach for error class detection, the proposed classifier shows promising generalization across different datasets and subjects without further adaptation. Moreover, by utilizing SEREEGA, we can systematically adjust parameters to accommodate the variability in the ErrP, facilitating the systematic validation of closed-loop setups. Furthermore, our objective is to develop a universal ErrP classifier that captures the signal's variability, enabling it to determine the presence or absence of an ErrP in real EEG data.$<$/p$>$
Omair Ali; Muhammad Saif-ur-Rehman; Marita Metzler; Tobias Glasmachers; Ioannis Iossifidis; Christian Klaes
GET: A Generative EEG Transformer for Continuous Context-Based Neural Signals Journal Article
In: arXiv:2406.03115 [q-bio], 2024.
Abstract | Links | BibTeX | Tags: BCI, EEG, Machine Learning, Quantitative Biology - Neurons and Cognition
@article{aliGETGenerativeEEG2024,
title = {GET: A Generative EEG Transformer for Continuous Context-Based Neural Signals},
author = {Omair Ali and Muhammad Saif-ur-Rehman and Marita Metzler and Tobias Glasmachers and Ioannis Iossifidis and Christian Klaes},
url = {http://arxiv.org/abs/2406.03115},
doi = {10.48550/arXiv.2406.03115},
year = {2024},
date = {2024-06-09},
urldate = {2024-06-09},
journal = {arXiv:2406.03115 [q-bio]},
abstract = {Generating continuous electroencephalography (EEG) signals through advanced artificial neural networks presents a novel opportunity to enhance brain-computer interface (BCI) technology. This capability has the potential to significantly enhance applications ranging from simulating dynamic brain activity and data augmentation to improving real-time epilepsy detection and BCI inference. By harnessing generative transformer neural networks, specifically designed for EEG signal generation, we can revolutionize the interpretation and interaction with neural data. Generative AI has demonstrated significant success across various domains, from natural language processing (NLP) and computer vision to content creation in visual arts and music. It distinguishes itself by using large-scale datasets to construct context windows during pre-training, a technique that has proven particularly effective in NLP, where models are fine-tuned for specific downstream tasks after extensive foundational training. However, the application of generative AI in the field of BCIs, particularly through the development of continuous, context-rich neural signal generators, has been limited. To address this, we introduce the Generative EEG Transformer (GET), a model leveraging transformer architecture tailored for EEG data. The GET model is pre-trained on diverse EEG datasets, including motor imagery and alpha wave datasets, enabling it to produce high-fidelity neural signals that maintain contextual integrity. Our empirical findings indicate that GET not only faithfully reproduces the frequency spectrum of the training data and input prompts but also robustly generates continuous neural signals. By adopting the successful training strategies of the NLP domain for BCIs, the GET sets a new standard for the development and application of neural signal generation technologies.},
keywords = {BCI, EEG, Machine Learning, Quantitative Biology - Neurons and Cognition},
pubstate = {published},
tppubtype = {article}
}
Generating continuous electroencephalography (EEG) signals through advanced artificial neural networks presents a novel opportunity to enhance brain-computer interface (BCI) technology. This capability has the potential to significantly enhance applications ranging from simulating dynamic brain activity and data augmentation to improving real-time epilepsy detection and BCI inference. By harnessing generative transformer neural networks, specifically designed for EEG signal generation, we can revolutionize the interpretation and interaction with neural data. Generative AI has demonstrated significant success across various domains, from natural language processing (NLP) and computer vision to content creation in visual arts and music. It distinguishes itself by using large-scale datasets to construct context windows during pre-training, a technique that has proven particularly effective in NLP, where models are fine-tuned for specific downstream tasks after extensive foundational training. However, the application of generative AI in the field of BCIs, particularly through the development of continuous, context-rich neural signal generators, has been limited. To address this, we introduce the Generative EEG Transformer (GET), a model leveraging transformer architecture tailored for EEG data. The GET model is pre-trained on diverse EEG datasets, including motor imagery and alpha wave datasets, enabling it to produce high-fidelity neural signals that maintain contextual integrity. Our empirical findings indicate that GET not only faithfully reproduces the frequency spectrum of the training data and input prompts but also robustly generates continuous neural signals. By adopting the successful training strategies of the NLP domain for BCIs, the GET sets a new standard for the development and application of neural signal generation technologies.
Omair Ali; Muhammad Saif-ur-Rehman; Marita Metzler; Tobias Glasmachers; Ioannis Iossifidis; Christian Klaes
GET: A Generative EEG Transformer for Continuous Context-Based Neural Signals Journal Article
In: arXiv:2406.03115 [q-bio], 2024.
Abstract | Links | BibTeX | Tags: BCI, EEG, Machine Learning, Quantitative Biology - Neurons and Cognition
@article{aliGETGenerativeEEG2024b,
title = {GET: A Generative EEG Transformer for Continuous Context-Based Neural Signals},
author = {Omair Ali and Muhammad Saif-ur-Rehman and Marita Metzler and Tobias Glasmachers and Ioannis Iossifidis and Christian Klaes},
url = {http://arxiv.org/abs/2406.03115},
doi = {10.48550/arXiv.2406.03115},
year = {2024},
date = {2024-06-09},
urldate = {2024-06-09},
journal = {arXiv:2406.03115 [q-bio]},
abstract = {Generating continuous electroencephalography (EEG) signals through advanced artificial neural networks presents a novel opportunity to enhance brain-computer interface (BCI) technology. This capability has the potential to significantly enhance applications ranging from simulating dynamic brain activity and data augmentation to improving real-time epilepsy detection and BCI inference. By harnessing generative transformer neural networks, specifically designed for EEG signal generation, we can revolutionize the interpretation and interaction with neural data. Generative AI has demonstrated significant success across various domains, from natural language processing (NLP) and computer vision to content creation in visual arts and music. It distinguishes itself by using large-scale datasets to construct context windows during pre-training, a technique that has proven particularly effective in NLP, where models are fine-tuned for specific downstream tasks after extensive foundational training. However, the application of generative AI in the field of BCIs, particularly through the development of continuous, context-rich neural signal generators, has been limited. To address this, we introduce the Generative EEG Transformer (GET), a model leveraging transformer architecture tailored for EEG data. The GET model is pre-trained on diverse EEG datasets, including motor imagery and alpha wave datasets, enabling it to produce high-fidelity neural signals that maintain contextual integrity. Our empirical findings indicate that GET not only faithfully reproduces the frequency spectrum of the training data and input prompts but also robustly generates continuous neural signals. By adopting the successful training strategies of the NLP domain for BCIs, the GET sets a new standard for the development and application of neural signal generation technologies.},
keywords = {BCI, EEG, Machine Learning, Quantitative Biology - Neurons and Cognition},
pubstate = {published},
tppubtype = {article}
}
Generating continuous electroencephalography (EEG) signals through advanced artificial neural networks presents a novel opportunity to enhance brain-computer interface (BCI) technology. This capability has the potential to significantly enhance applications ranging from simulating dynamic brain activity and data augmentation to improving real-time epilepsy detection and BCI inference. By harnessing generative transformer neural networks, specifically designed for EEG signal generation, we can revolutionize the interpretation and interaction with neural data. Generative AI has demonstrated significant success across various domains, from natural language processing (NLP) and computer vision to content creation in visual arts and music. It distinguishes itself by using large-scale datasets to construct context windows during pre-training, a technique that has proven particularly effective in NLP, where models are fine-tuned for specific downstream tasks after extensive foundational training. However, the application of generative AI in the field of BCIs, particularly through the development of continuous, context-rich neural signal generators, has been limited. To address this, we introduce the Generative EEG Transformer (GET), a model leveraging transformer architecture tailored for EEG data. The GET model is pre-trained on diverse EEG datasets, including motor imagery and alpha wave datasets, enabling it to produce high-fidelity neural signals that maintain contextual integrity. Our empirical findings indicate that GET not only faithfully reproduces the frequency spectrum of the training data and input prompts but also robustly generates continuous neural signals. By adopting the successful training strategies of the NLP domain for BCIs, the GET sets a new standard for the development and application of neural signal generation technologies.
2023
Omair Ali; Muhammad Saif-ur-Rehman; Tobias Glasmachers; Ioannis Iossifidis; Christian Klaes
ConTraNet: A Hybrid Network for Improving the Classification of EEG and EMG Signals with Limited Training Data Journal Article
In: Computers in Biology and Medicine, pp. 107649, 2023, ISSN: 0010-4825.
Abstract | Links | BibTeX | Tags: Brain computer interface, Deep learning, EEG decoding, EMG decoding, Machine Learning
@article{aliConTraNetHybridNetwork2023,
title = {ConTraNet: A Hybrid Network for Improving the Classification of EEG and EMG Signals with Limited Training Data},
author = {Omair Ali and Muhammad Saif-ur-Rehman and Tobias Glasmachers and Ioannis Iossifidis and Christian Klaes},
url = {https://www.sciencedirect.com/science/article/pii/S0010482523011149},
doi = {10.1016/j.compbiomed.2023.107649},
issn = {0010-4825},
year = {2023},
date = {2023-11-02},
urldate = {2023-11-02},
journal = {Computers in Biology and Medicine},
pages = {107649},
abstract = {Objective Bio-Signals such as electroencephalography (EEG) and electromyography (EMG) are widely used for the rehabilitation of physically disabled people and for the characterization of cognitive impairments. Successful decoding of these bio-signals is however non-trivial because of the time-varying and non-stationary characteristics. Furthermore, existence of short- and long-range dependencies in these time-series signal makes the decoding even more challenging. State-of-the-art studies proposed Convolutional Neural Networks (CNNs) based architectures for the classification of these bio-signals, which are proven useful to learn spatial representations. However, CNNs because of the fixed size convolutional kernels and shared weights pay only uniform attention and are also suboptimal in learning short-long term dependencies, simultaneously, which could be pivotal in decoding EEG and EMG signals. Therefore, it is important to address these limitations of CNNs. To learn short- and long-range dependencies simultaneously and to pay more attention to more relevant part of the input signal, Transformer neural network-based architectures can play a significant role. Nonetheless, it requires a large corpus of training data. However, EEG and EMG decoding studies produce limited amount of the data. Therefore, using standalone transformers neural networks produce ordinary results. In this study, we ask a question whether we can fix the limitations of CNN and transformer neural networks and provide a robust and generalized model that can simultaneously learn spatial patterns, long-short term dependencies, pay variable amount of attention to time-varying non-stationary input signal with limited training data. Approach In this work, we introduce a novel single hybrid model called ConTraNet, which is based on CNN and Transformer architectures that contains the strengths of both CNN and Transformer neural networks. ConTraNet uses a CNN block to introduce inductive bias in the model and learn local dependencies, whereas the Transformer block uses the self-attention mechanism to learn the short- and long-range or global dependencies in the signal and learn to pay different attention to different parts of the signals. Main results We evaluated and compared the ConTraNet with state-of-the-art methods on four publicly available datasets (BCI Competition IV dataset 2b, Physionet MI-EEG dataset, Mendeley sEMG dataset, Mendeley sEMG V1 dataset) which belong to EEG-HMI and EMG-HMI paradigms. ConTraNet outperformed its counterparts in all the different category tasks (2-class, 3-class, 4-class, 7-class, and 10-class decoding tasks). Significance With limited training data ConTraNet significantly improves classification performance on four publicly available datasets for 2, 3, 4, 7, and 10-classes compared to its counterparts.},
keywords = {Brain computer interface, Deep learning, EEG decoding, EMG decoding, Machine Learning},
pubstate = {published},
tppubtype = {article}
}
Objective Bio-Signals such as electroencephalography (EEG) and electromyography (EMG) are widely used for the rehabilitation of physically disabled people and for the characterization of cognitive impairments. Successful decoding of these bio-signals is however non-trivial because of the time-varying and non-stationary characteristics. Furthermore, existence of short- and long-range dependencies in these time-series signal makes the decoding even more challenging. State-of-the-art studies proposed Convolutional Neural Networks (CNNs) based architectures for the classification of these bio-signals, which are proven useful to learn spatial representations. However, CNNs because of the fixed size convolutional kernels and shared weights pay only uniform attention and are also suboptimal in learning short-long term dependencies, simultaneously, which could be pivotal in decoding EEG and EMG signals. Therefore, it is important to address these limitations of CNNs. To learn short- and long-range dependencies simultaneously and to pay more attention to more relevant part of the input signal, Transformer neural network-based architectures can play a significant role. Nonetheless, it requires a large corpus of training data. However, EEG and EMG decoding studies produce limited amount of the data. Therefore, using standalone transformers neural networks produce ordinary results. In this study, we ask a question whether we can fix the limitations of CNN and transformer neural networks and provide a robust and generalized model that can simultaneously learn spatial patterns, long-short term dependencies, pay variable amount of attention to time-varying non-stationary input signal with limited training data. Approach In this work, we introduce a novel single hybrid model called ConTraNet, which is based on CNN and Transformer architectures that contains the strengths of both CNN and Transformer neural networks. ConTraNet uses a CNN block to introduce inductive bias in the model and learn local dependencies, whereas the Transformer block uses the self-attention mechanism to learn the short- and long-range or global dependencies in the signal and learn to pay different attention to different parts of the signals. Main results We evaluated and compared the ConTraNet with state-of-the-art methods on four publicly available datasets (BCI Competition IV dataset 2b, Physionet MI-EEG dataset, Mendeley sEMG dataset, Mendeley sEMG V1 dataset) which belong to EEG-HMI and EMG-HMI paradigms. ConTraNet outperformed its counterparts in all the different category tasks (2-class, 3-class, 4-class, 7-class, and 10-class decoding tasks). Significance With limited training data ConTraNet significantly improves classification performance on four publicly available datasets for 2, 3, 4, 7, and 10-classes compared to its counterparts.
Marie D. Schmidt; Tobias Glasmachers; Ioannis Iossifidis
The Concepts of Muscle Activity Generation Driven by Upper Limb Kinematics Journal Article
In: BioMedical Engineering OnLine, vol. 22, no. 1, pp. 63, 2023, ISSN: 1475-925X.
Abstract | Links | BibTeX | Tags: Artificial generated signal, BCI, Electromyography (EMG), Generative model, Inertial measurement unit (IMU), Machine Learning, Motion parameters, Muscle activity, Neural networks, transfer learning, Voluntary movement
@article{schmidtConceptsMuscleActivity2023,
title = {The Concepts of Muscle Activity Generation Driven by Upper Limb Kinematics},
author = {Marie D. Schmidt and Tobias Glasmachers and Ioannis Iossifidis},
url = {https://doi.org/10.1186/s12938-023-01116-9},
doi = {10.1186/s12938-023-01116-9},
issn = {1475-925X},
year = {2023},
date = {2023-06-24},
urldate = {2023-06-24},
journal = {BioMedical Engineering OnLine},
volume = {22},
number = {1},
pages = {63},
abstract = {The underlying motivation of this work is to demonstrate that artificial muscle activity of known and unknown motion can be generated based on motion parameters, such as angular position, acceleration, and velocity of each joint (or the end-effector instead), which are similarly represented in our brains. This model is motivated by the known motion planning process in the central nervous system. That process incorporates the current body state from sensory systems and previous experiences, which might be represented as pre-learned inverse dynamics that generate associated muscle activity.},
keywords = {Artificial generated signal, BCI, Electromyography (EMG), Generative model, Inertial measurement unit (IMU), Machine Learning, Motion parameters, Muscle activity, Neural networks, transfer learning, Voluntary movement},
pubstate = {published},
tppubtype = {article}
}
The underlying motivation of this work is to demonstrate that artificial muscle activity of known and unknown motion can be generated based on motion parameters, such as angular position, acceleration, and velocity of each joint (or the end-effector instead), which are similarly represented in our brains. This model is motivated by the known motion planning process in the central nervous system. That process incorporates the current body state from sensory systems and previous experiences, which might be represented as pre-learned inverse dynamics that generate associated muscle activity.
Muhammad Saif-ur-Rehman; Omair Ali; Christian Klaes; Ioannis Iossifidis
Adaptive SpikeDeep-Classifier: Self-organizing and self-supervised machine learning algorithm for online spike sorting Journal Article
In: arXiv:2304.01355 [cs, math, q-bio], 2023.
Links | BibTeX | Tags: BCI, Machine Learning, Spike Sorting
@article{saifurrehman2023adaptive,
title = {Adaptive SpikeDeep-Classifier: Self-organizing and self-supervised machine learning algorithm for online spike sorting},
author = {Muhammad Saif-ur-Rehman and Omair Ali and Christian Klaes and Ioannis Iossifidis},
doi = {10.48550/arXiv.2304.01355},
year = {2023},
date = {2023-05-02},
urldate = {2023-05-02},
journal = {arXiv:2304.01355 [cs, math, q-bio]},
keywords = {BCI, Machine Learning, Spike Sorting},
pubstate = {published},
tppubtype = {article}
}
2022
Felix Grün; Muhammad Saif-ur-Rehman; Tobias Glasmachers; Ioannis Iossifidis
Invariance to Quantile Selection in Distributional Continuous Control Journal Article
In: arXiv:2212.14262 [cs.LG], 2022.
Links | BibTeX | Tags: Artificial Intelligence (cs.AI), FOS: Computer and information sciences, I.2.6, I.2.8, Machine Learning, Machine Learning (cs.LG)
@article{grunInvarianceQuantileSelection2022,
title = {Invariance to Quantile Selection in Distributional Continuous Control},
author = {Felix Grün and Muhammad Saif-ur-Rehman and Tobias Glasmachers and Ioannis Iossifidis},
url = {https://arxiv.org/abs/2212.14262},
doi = {10.48550/ARXIV.2212.14262},
year = {2022},
date = {2022-12-29},
urldate = {2022-12-29},
journal = {arXiv:2212.14262 [cs.LG]},
keywords = {Artificial Intelligence (cs.AI), FOS: Computer and information sciences, I.2.6, I.2.8, Machine Learning, Machine Learning (cs.LG)},
pubstate = {published},
tppubtype = {article}
}
Stephan Johann Lehmler; Muhammad Saif-ur-Rehman; Tobias Glasmachers; Ioannis Iossifidis
Deep transfer learning compared to subject-specific models for sEMG decoders Journal Article
In: Journal of Neural Engineering, 2022.
Links | BibTeX | Tags: BCI, Computational Complexity, Deep Transfer-Learning, Machine Learning, transfer learning
@article{lehmlerTransferLearningPatientSpecific2021bb,
title = {Deep transfer learning compared to subject-specific models for sEMG decoders},
author = {Stephan Johann Lehmler and Muhammad Saif-ur-Rehman and Tobias Glasmachers and Ioannis Iossifidis},
url = {http://iopscience.iop.org/article/10.1088/1741-2552/ac9860},
doi = {10.1088/1741-2552/ac9860},
year = {2022},
date = {2022-10-28},
urldate = {2022-10-28},
journal = {Journal of Neural Engineering},
keywords = {BCI, Computational Complexity, Deep Transfer-Learning, Machine Learning, transfer learning},
pubstate = {published},
tppubtype = {article}
}
Felix Grün; Ioannis Iossifidis
Exploring Distribution Parameterizations for Distributional Continuous Control Proceedings Article
In: BC22 : Computational Neuroscience & Neurotechnology Bernstein Conference 2022, BCCN Bernstein Network Computational Network, 2022.
Links | BibTeX | Tags: Machine Learning, Reinforcement learning
@inproceedings{grunExploringDistributionParameterizations2022,
title = {Exploring Distribution Parameterizations for Distributional Continuous Control},
author = {Felix Grün and Ioannis Iossifidis},
doi = {10.12751/nncn.bc2022.112},
year = {2022},
date = {2022-09-15},
urldate = {2022-09-15},
booktitle = {BC22 : Computational Neuroscience & Neurotechnology Bernstein Conference 2022},
publisher = {BCCN Bernstein Network Computational Network},
keywords = {Machine Learning, Reinforcement learning},
pubstate = {published},
tppubtype = {inproceedings}
}
Stephan Johann Lehmler; Muhammad Saif-ur-Rehman; Ioannis Iossifidis
Modeling Subject Specfic Surface EMG Features by Means of Deep Learning Proceedings Article
In: BC22 : Computational Neuroscience & Neurotechnology Bernstein Conference 2022, BCCN Bernstein Network Computational Network, 2022.
Links | BibTeX | Tags: BCI, Machine Learning
@inproceedings{lehmlerModelingSubjectSpecfic2022,
title = {Modeling Subject Specfic Surface EMG Features by Means of Deep Learning},
author = {Stephan Johann Lehmler and Muhammad Saif-ur-Rehman and Ioannis Iossifidis},
doi = {10.12751/nncn.bc2022.309},
year = {2022},
date = {2022-09-15},
urldate = {2022-09-15},
booktitle = {BC22 : Computational Neuroscience & Neurotechnology Bernstein Conference 2022},
publisher = {BCCN Bernstein Network Computational Network},
keywords = {BCI, Machine Learning},
pubstate = {published},
tppubtype = {inproceedings}
}
Marie Dominique Schmidt; Ioannis Iossifidis
Linking Muscle Activity and Motion Trajectory Proceedings Article
In: BC22 : Computational Neuroscience & Neurotechnology Bernstein Conference 2022, BCCN Bernstein Network Computational Network, 2022.
Links | BibTeX | Tags: BCI, Machine Learning
@inproceedings{schmidtLinkingMuscleActivity2022,
title = {Linking Muscle Activity and Motion Trajectory},
author = {Marie Dominique Schmidt and Ioannis Iossifidis},
doi = {10.12751/nncn.bc2022.191},
year = {2022},
date = {2022-09-15},
urldate = {2022-09-15},
booktitle = {BC22 : Computational Neuroscience & Neurotechnology Bernstein Conference 2022},
publisher = {BCCN Bernstein Network Computational Network},
keywords = {BCI, Machine Learning},
pubstate = {published},
tppubtype = {inproceedings}
}
Tim Sziburis; Susanne Blex; Ioannis Iossifidis
A Dataset of 3D Hand Transport Trajectories Determined by Inertial Measurements from a Single Sensor Proceedings Article
In: BC22 : Computational Neuroscience & Neurotechnology Bernstein Conference 2022, BCCN Bernstein Network Computational Network, 2022.
Links | BibTeX | Tags: BCI, Machine Learning
@inproceedings{sziburisDataset3DHand2022,
title = {A Dataset of 3D Hand Transport Trajectories Determined by Inertial Measurements from a Single Sensor},
author = {Tim Sziburis and Susanne Blex and Ioannis Iossifidis},
doi = {10.12751/nncn.bc2022.186},
year = {2022},
date = {2022-09-15},
urldate = {2022-09-15},
booktitle = {BC22 : Computational Neuroscience & Neurotechnology Bernstein Conference 2022},
publisher = {BCCN Bernstein Network Computational Network},
keywords = {BCI, Machine Learning},
pubstate = {published},
tppubtype = {inproceedings}
}
Aline Xavier Fidencio; Christian Klaes; Ioannis Iossifidis
Closed-Loop Adaptation of Brain-Machine Interfaces Using Error-Related Potentials and Reinforcement Learning Proceedings Article
In: BC22 : Computational Neuroscience & Neurotechnology Bernstein Conference 2022, BCCN Bernstein Network Computational Network, 2022.
Links | BibTeX | Tags: BCI, Machine Learning
@inproceedings{xavierfidencioClosedloopAdaptationBrainmachine2022,
title = {Closed-Loop Adaptation of Brain-Machine Interfaces Using Error-Related Potentials and Reinforcement Learning},
author = {Aline Xavier Fidencio and Christian Klaes and Ioannis Iossifidis},
doi = {10.12751/nncn.bc2022.136},
year = {2022},
date = {2022-09-15},
urldate = {2022-09-15},
booktitle = {BC22 : Computational Neuroscience & Neurotechnology Bernstein Conference 2022},
publisher = {BCCN Bernstein Network Computational Network},
keywords = {BCI, Machine Learning},
pubstate = {published},
tppubtype = {inproceedings}
}
Aline Xavier Fidencio; Christian Klaes; Ioannis Iossifidis
Error-Related Potentials in Reinforcement Learning-Based Brain-Machine Interfaces Journal Article
In: Frontiers in Human Neuroscience, vol. 16, 2022.
Abstract | Links | BibTeX | Tags: BCI, EEG, error-related potentials, Machine Learning, Reinforcement learning
@article{xavierfidencioErrorrelated,
title = {Error-Related Potentials in Reinforcement Learning-Based Brain-Machine Interfaces},
author = {Aline Xavier Fidencio and Christian Klaes and Ioannis Iossifidis},
url = {https://www.frontiersin.org/article/10.3389/fnhum.2022.806517},
doi = {https://doi.org/10.3389/fnhum.2022.806517},
year = {2022},
date = {2022-06-24},
urldate = {2022-06-24},
journal = {Frontiers in Human Neuroscience},
volume = {16},
abstract = {The human brain has been an object of extensive investigation in different fields. While several studies have focused on understanding the neural correlates of error processing, advances in brain-machine interface systems using non-invasive techniques further enabled the use of the measured signals in different applications. The possibility of detecting these error-related potentials (ErrPs) under different experimental setups on a single-trial basis has further increased interest in their integration in closed-loop settings to improve system performance, for example, by performing error correction. Fewer works have, however, aimed at reducing future mistakes or learning. We present a review focused on the current literature using non-invasive systems that have combined the ErrPs information specifically in a reinforcement learning framework to go beyond error correction and have used these signals for learning.},
keywords = {BCI, EEG, error-related potentials, Machine Learning, Reinforcement learning},
pubstate = {published},
tppubtype = {article}
}
The human brain has been an object of extensive investigation in different fields. While several studies have focused on understanding the neural correlates of error processing, advances in brain-machine interface systems using non-invasive techniques further enabled the use of the measured signals in different applications. The possibility of detecting these error-related potentials (ErrPs) under different experimental setups on a single-trial basis has further increased interest in their integration in closed-loop settings to improve system performance, for example, by performing error correction. Fewer works have, however, aimed at reducing future mistakes or learning. We present a review focused on the current literature using non-invasive systems that have combined the ErrPs information specifically in a reinforcement learning framework to go beyond error correction and have used these signals for learning.
Omair Ali; Muhammad Saif-ur-Rehman; Tobias Glasmachers; Ioannis Iossifidis; Christian Klaes
ConTraNet: A Single End-to-End Hybrid Network for EEG-based and EMG-based Human Machine Interfaces Journal Article
In: arXiv:2206.10677 [q-bio.NC], 2022.
Abstract | Links | BibTeX | Tags: BCI, Machine Learning, neural processing, signal processing
@article{aliConTraNetSingleEndtoend2022,
title = {ConTraNet: A Single End-to-End Hybrid Network for EEG-based and EMG-based Human Machine Interfaces},
author = {Omair Ali and Muhammad Saif-ur-Rehman and Tobias Glasmachers and Ioannis Iossifidis and Christian Klaes},
url = {http://arxiv.org/abs/2206.10677},
doi = {10.48550/arXiv.2206.10677},
year = {2022},
date = {2022-06-21},
urldate = {2022-06-21},
journal = {arXiv:2206.10677 [q-bio.NC]},
abstract = {Objective: Electroencephalography (EEG) and electromyography (EMG) are two non-invasive bio-signals, which are widely used in human machine interface (HMI) technologies (EEG-HMI and EMG-HMI paradigm) for the rehabilitation of physically disabled people. Successful decoding of EEG and EMG signals into respective control command is a pivotal step in the rehabilitation process. Recently, several Convolutional neural networks (CNNs) based architectures are proposed that directly map the raw time-series signal into decision space and the process of meaningful features extraction and classification are performed simultaneously. However, these networks are tailored to the learn the expected characteristics of the given bio-signal and are limited to single paradigm. In this work, we addressed the question that can we build a single architecture which is able to learn distinct features from different HMI paradigms and still successfully classify them. Approach: In this work, we introduce a single hybrid model called ConTraNet, which is based on CNN and Transformer architectures that is equally useful for EEG-HMI and EMG-HMI paradigms. ConTraNet uses CNN block to introduce inductive bias in the model and learn local dependencies, whereas the Transformer block uses the self-attention mechanism to learn the long-range dependencies in the signal, which are crucial for the classification of EEG and EMG signals. Main results: We evaluated and compared the ConTraNet with state-of-the-art methods on three publicly available datasets which belong to EEG-HMI and EMG-HMI paradigms. ConTraNet outperformed its counterparts in all the different category tasks (2-class, 3-class, 4-class, and 10-class decoding tasks). Significance: The results suggest that ConTraNet is robust to learn distinct features from different HMI paradigms and generalizes well as compared to the current state of the art algorithms.},
keywords = {BCI, Machine Learning, neural processing, signal processing},
pubstate = {published},
tppubtype = {article}
}
Objective: Electroencephalography (EEG) and electromyography (EMG) are two non-invasive bio-signals, which are widely used in human machine interface (HMI) technologies (EEG-HMI and EMG-HMI paradigm) for the rehabilitation of physically disabled people. Successful decoding of EEG and EMG signals into respective control command is a pivotal step in the rehabilitation process. Recently, several Convolutional neural networks (CNNs) based architectures are proposed that directly map the raw time-series signal into decision space and the process of meaningful features extraction and classification are performed simultaneously. However, these networks are tailored to the learn the expected characteristics of the given bio-signal and are limited to single paradigm. In this work, we addressed the question that can we build a single architecture which is able to learn distinct features from different HMI paradigms and still successfully classify them. Approach: In this work, we introduce a single hybrid model called ConTraNet, which is based on CNN and Transformer architectures that is equally useful for EEG-HMI and EMG-HMI paradigms. ConTraNet uses CNN block to introduce inductive bias in the model and learn local dependencies, whereas the Transformer block uses the self-attention mechanism to learn the long-range dependencies in the signal, which are crucial for the classification of EEG and EMG signals. Main results: We evaluated and compared the ConTraNet with state-of-the-art methods on three publicly available datasets which belong to EEG-HMI and EMG-HMI paradigms. ConTraNet outperformed its counterparts in all the different category tasks (2-class, 3-class, 4-class, and 10-class decoding tasks). Significance: The results suggest that ConTraNet is robust to learn distinct features from different HMI paradigms and generalizes well as compared to the current state of the art algorithms.
Omair Ali; Muhammad Saif-ur-Rehman; Susanne Dyck; Tobias Glasmachers; Ioannis Iossifidis; Christian Klaes
Enhancing the decoding accuracy of EEG signals by the introduction of anchored-STFT and adversarial data augmentation method Journal Article
In: Nature Scientific Reports, vol. 12, iss. 1, pp. 4245, 2022, ISSN: 2045-2322.
Abstract | Links | BibTeX | Tags: Adversarial NN, BCI, computer science, EEG, Machine Learning, Quantitative Biology, Quantitative Methods
@article{aliAnchoredSTFTGNAAExtension2021a,
title = {Enhancing the decoding accuracy of EEG signals by the introduction of anchored-STFT and adversarial data augmentation method},
author = {Omair Ali and Muhammad Saif-ur-Rehman and Susanne Dyck and Tobias Glasmachers and Ioannis Iossifidis and Christian Klaes},
url = {https://www.nature.com/articles/s41598-022-07992-w},
doi = {https://doi.org/10.1038/s41598-022-07992-w},
issn = {2045-2322},
year = {2022},
date = {2022-03-10},
urldate = {2022-03-10},
journal = {Nature Scientific Reports},
volume = {12},
issue = {1},
pages = {4245},
abstract = {Brain-computer interfaces (BCIs) enable communication between humans and machines by translating brain activity into control commands. Electroencephalography (EEG) signals are one of the most used brain signals in non-invasive BCI applications but are often contaminated with noise. Therefore, it is possible that meaningful patterns for classifying EEG signals are deeply hidden. State-of-the-art deep-learning algorithms are successful in learning hidden, meaningful patterns. However, the quality and the quantity of the presented inputs is pivotal. Here, we propose a novel feature extraction method called anchored Short Time Fourier Transform (anchored-STFT), which is an advanced version of STFT, as it minimizes the trade-off between temporal and spectral resolution presented by STFT. In addition, we propose a novel augmentation method, called gradient norm adversarial augmentation (GNAA). GNAA is not only an augmentation method but is also used to harness adversarial inputs in EEG data, which not only improves the classification accuracy but also enhances the robustness of the classifier. In addition, we also propose a new CNN architecture, namely Skip-Net, for the classification of EEG signals. The proposed pipeline outperforms all state-of-the-art methods and yields an average classification accuracy of 90.7 % and 89.54 % on BCI competition II dataset III and BCI competition IV dataset 2b, respectively.},
keywords = {Adversarial NN, BCI, computer science, EEG, Machine Learning, Quantitative Biology, Quantitative Methods},
pubstate = {published},
tppubtype = {article}
}
Brain-computer interfaces (BCIs) enable communication between humans and machines by translating brain activity into control commands. Electroencephalography (EEG) signals are one of the most used brain signals in non-invasive BCI applications but are often contaminated with noise. Therefore, it is possible that meaningful patterns for classifying EEG signals are deeply hidden. State-of-the-art deep-learning algorithms are successful in learning hidden, meaningful patterns. However, the quality and the quantity of the presented inputs is pivotal. Here, we propose a novel feature extraction method called anchored Short Time Fourier Transform (anchored-STFT), which is an advanced version of STFT, as it minimizes the trade-off between temporal and spectral resolution presented by STFT. In addition, we propose a novel augmentation method, called gradient norm adversarial augmentation (GNAA). GNAA is not only an augmentation method but is also used to harness adversarial inputs in EEG data, which not only improves the classification accuracy but also enhances the robustness of the classifier. In addition, we also propose a new CNN architecture, namely Skip-Net, for the classification of EEG signals. The proposed pipeline outperforms all state-of-the-art methods and yields an average classification accuracy of 90.7 % and 89.54 % on BCI competition II dataset III and BCI competition IV dataset 2b, respectively.
Marie Dominique Schmidt; Tobias Glasmachers; Ioannis Iossifidis
From Motion to Muscle Journal Article
In: arXiv: 2201.11501 [cs.LG], 2022.
Links | BibTeX | Tags: BCI, Machine Learning, movement model, muscle signal generator, recurrent neural network
@article{schmidt2022motion,
title = {From Motion to Muscle},
author = {Marie Dominique Schmidt and Tobias Glasmachers and Ioannis Iossifidis},
doi = {https://doi.org/10.48550/arXiv.2201.11501},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
journal = {arXiv: 2201.11501 [cs.LG]},
keywords = {BCI, Machine Learning, movement model, muscle signal generator, recurrent neural network},
pubstate = {published},
tppubtype = {article}
}
Aline Xavier Fidencio; Tobias Glasmachers; Ioannis Iossifidis
Error-Related Potentials Detection with Dry- and Wet-Electrode EEG Proceedings Article
In: FENS, Forum 2022, FENS, Federation of European Neuroscience Societies, 2022.
Abstract | BibTeX | Tags: BCI, EEG, error-related potentials, Machine Learning
@inproceedings{fidencioErrorrelatedPotentialsDetection2022,
title = {Error-Related Potentials Detection with Dry- and Wet-Electrode EEG},
author = {Aline Xavier Fidencio and Tobias Glasmachers and Ioannis Iossifidis},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
booktitle = {FENS, Forum 2022},
publisher = {FENS, Federation of European Neuroscience Societies},
abstract = {Electroencephalography (EEG) is a non-invasive technique for measuring brain electrical activity from electrodes placed on the scalp surface. Improvements in this technology are particularly relevant because they also boost brain-machine interfaces (BMI) development. Commonly, gel-based electrodes are used since they guarantee a high-quality signal. Alternatively, dry electrodes have been introduced, more suitable for daily use. In this work, we compare conventional dry and wet electrode systems specifically for the detection of error-related potentials (ErrPs). ErrPs are elicited as a reaction to both self-made and external errors. There has been increased interest in the integration of these signals into BMIs to improve their performance since they provide a convenient source of feedback to the system with no extra workload for the subject. These signals can be used, e.g., to correct errors or even for system adaptation. ErrP-based BMIs in the literature have consistently used wet electrodes. Therefore, even though both electrodes types have been compared for other event-related potentials (e.g., P300), it is relevant to know whether the signal quality for the detection of ErrPs is comparable among them. In this work, we implement a simple game to elicit ErrPs and compare the quality of the measured signals. We tested the feasibility of the experimental protocol to elicit ErrP and the measured ErrP displayed a similar waveshape in terms of observed peaks. However, differences exist in both latencies as well as in their amplitude. These variations and other relevant characteristics have to be further verified with more subjects},
keywords = {BCI, EEG, error-related potentials, Machine Learning},
pubstate = {published},
tppubtype = {inproceedings}
}
Electroencephalography (EEG) is a non-invasive technique for measuring brain electrical activity from electrodes placed on the scalp surface. Improvements in this technology are particularly relevant because they also boost brain-machine interfaces (BMI) development. Commonly, gel-based electrodes are used since they guarantee a high-quality signal. Alternatively, dry electrodes have been introduced, more suitable for daily use. In this work, we compare conventional dry and wet electrode systems specifically for the detection of error-related potentials (ErrPs). ErrPs are elicited as a reaction to both self-made and external errors. There has been increased interest in the integration of these signals into BMIs to improve their performance since they provide a convenient source of feedback to the system with no extra workload for the subject. These signals can be used, e.g., to correct errors or even for system adaptation. ErrP-based BMIs in the literature have consistently used wet electrodes. Therefore, even though both electrodes types have been compared for other event-related potentials (e.g., P300), it is relevant to know whether the signal quality for the detection of ErrPs is comparable among them. In this work, we implement a simple game to elicit ErrPs and compare the quality of the measured signals. We tested the feasibility of the experimental protocol to elicit ErrP and the measured ErrP displayed a similar waveshape in terms of observed peaks. However, differences exist in both latencies as well as in their amplitude. These variations and other relevant characteristics have to be further verified with more subjects
Marie Dominique Schmidt; Tobias Glasmachers; Ioannis Iossifidis
Motion Intention Prediction Proceedings Article
In: FENS, Forum 2022, FENS, Federation of European Neuroscience Societies, 2022.
Abstract | BibTeX | Tags: BCI, Machine Learning, movement model, muscle signal generator, recurrent neural network
@inproceedings{schmidtMotionIntentionPrediction2022a,
title = {Motion Intention Prediction},
author = {Marie Dominique Schmidt and Tobias Glasmachers and Ioannis Iossifidis},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
booktitle = {FENS, Forum 2022},
publisher = {FENS, Federation of European Neuroscience Societies},
abstract = {Motion intention prediction is the key to robot-assisted rehabilitation systems. These can rely on various biological signals. One commonly used signal is the muscle activity measured by an electromyogram that occurs between 50-100 milliseconds before the actual movement, allowing a real-world application to assist in time. We show that upper limb motion can be estimated from the corresponding muscle activity. To this end, eight-arm muscles are mapped to the joint angle, velocity, and acceleration of the shoulder, elbow, and wrist. For this purpose, we specifically develop an artificial neural network that estimates complex motions involving multiple upper limb joints. The network model is evaluated concerning its ability to generalize across subjects as well as for new motions. This is achieved through training on multiple subjects and additional transfer learning methods so that the prediction for new subjects is significantly improved. In particular, this is beneficial for a robust real-world application. Furthermore, we investigate the importance of the different parameters such as angle, velocity, and acceleration for simple and complex motions. Predictions for simple motions along with the main components of complex motions achieve excellent accuracy while joints that do not play a dominant role during the motion have comparatively lower accuracy.},
keywords = {BCI, Machine Learning, movement model, muscle signal generator, recurrent neural network},
pubstate = {published},
tppubtype = {inproceedings}
}
Motion intention prediction is the key to robot-assisted rehabilitation systems. These can rely on various biological signals. One commonly used signal is the muscle activity measured by an electromyogram that occurs between 50-100 milliseconds before the actual movement, allowing a real-world application to assist in time. We show that upper limb motion can be estimated from the corresponding muscle activity. To this end, eight-arm muscles are mapped to the joint angle, velocity, and acceleration of the shoulder, elbow, and wrist. For this purpose, we specifically develop an artificial neural network that estimates complex motions involving multiple upper limb joints. The network model is evaluated concerning its ability to generalize across subjects as well as for new motions. This is achieved through training on multiple subjects and additional transfer learning methods so that the prediction for new subjects is significantly improved. In particular, this is beneficial for a robust real-world application. Furthermore, we investigate the importance of the different parameters such as angle, velocity, and acceleration for simple and complex motions. Predictions for simple motions along with the main components of complex motions achieve excellent accuracy while joints that do not play a dominant role during the motion have comparatively lower accuracy.
2021
Felix Grün; Tobias Glasmachers; Ioannis Iossifidis
Off-Policy Continuous Control Using Distributional Reinforcement Learning Proceedings Article
In: Bernstein Conference, 2021.
Links | BibTeX | Tags: Machine Learning, Reinforcement learning
@inproceedings{grunOffPolicyContinuousControl2021b,
title = {Off-Policy Continuous Control Using Distributional Reinforcement Learning},
author = {Felix Grün and Tobias Glasmachers and Ioannis Iossifidis},
doi = {10.12751/nncn.bc2021.p001},
year = {2021},
date = {2021-10-01},
urldate = {2021-10-01},
publisher = {Bernstein Conference},
keywords = {Machine Learning, Reinforcement learning},
pubstate = {published},
tppubtype = {inproceedings}
}
Stephan Johann Lehmler; Muhammad Saif-ur-Rehman; Tobias Glasmachers; Ioannis Iossifidis
Transfer-Learning for Patient Specific Model Re-Calibration: Application to sEMG-Classification Proceedings Article
In: Bernstein Conferen, 2021.
Links | BibTeX | Tags: BCI, Computational Complexity, Deep Transfer-Learning, Machine Learning, transfer learning
@inproceedings{lehmlerTransferLearningPatientSpecific2021b,
title = {Transfer-Learning for Patient Specific Model Re-Calibration: Application to sEMG-Classification},
author = {Stephan Johann Lehmler and Muhammad Saif-ur-Rehman and Tobias Glasmachers and Ioannis Iossifidis},
doi = {10.12751/nncn.bc2021.p005},
year = {2021},
date = {2021-10-01},
urldate = {2021-10-01},
publisher = {Bernstein Conferen},
keywords = {BCI, Computational Complexity, Deep Transfer-Learning, Machine Learning, transfer learning},
pubstate = {published},
tppubtype = {inproceedings}
}
Marie Dominique Schmidt; Tobias Glasmachers; Ioannis Iossifidis
Artificially Generated Muscle Signals Proceedings Article
In: Bernstein Conference, 2021.
Links | BibTeX | Tags: BCI, Machine Learning, movement model, muscle signal generator, recurrent neural network
@inproceedings{schmidtArtificiallyGeneratedMuscle2021b,
title = {Artificially Generated Muscle Signals},
author = {Marie Dominique Schmidt and Tobias Glasmachers and Ioannis Iossifidis},
doi = {10.12751/nncn.bc2021.p111},
year = {2021},
date = {2021-10-01},
urldate = {2021-10-01},
publisher = {Bernstein Conference},
keywords = {BCI, Machine Learning, movement model, muscle signal generator, recurrent neural network},
pubstate = {published},
tppubtype = {inproceedings}
}
Aline Xavier Fidencio; Tobias Glasmachers; Christian Klaes; Ioannis Iossifidis
Beyond Error Correction: Integration of Error-Related Potentials into Brain-Computer Interfaces for Improved Performance Proceedings Article
In: Bernstein Conference, 2021.
Links | BibTeX | Tags: BCI, error-related potentials, Machine Learning, Reinforcement learning
@inproceedings{xavierfidencioErrorCorrectionIntegration2021b,
title = {Beyond Error Correction: Integration of Error-Related Potentials into Brain-Computer Interfaces for Improved Performance},
author = {Aline Xavier Fidencio and Tobias Glasmachers and Christian Klaes and Ioannis Iossifidis},
doi = {10.12751/nncn.bc2021.p163},
year = {2021},
date = {2021-10-01},
urldate = {2021-10-01},
publisher = {Bernstein Conference},
keywords = {BCI, error-related potentials, Machine Learning, Reinforcement learning},
pubstate = {published},
tppubtype = {inproceedings}
}
Felix Grün; Tobias Glasmachers; Ioannis Iossifidis
Off-Policy Continuous Control Using Distributional Reinforcement Learning Proceedings Article
In: BC21 : Computational Neuroscience & Neurotechnology Bernstein Conference 2021, BCCN Bernstein Network Computational Network, 2021.
Links | BibTeX | Tags: Machine Learning, Reinforcement learning
@inproceedings{grunOffPolicyContinuousControl2021,
title = {Off-Policy Continuous Control Using Distributional Reinforcement Learning},
author = {Felix Grün and Tobias Glasmachers and Ioannis Iossifidis},
doi = {10.12751/nncn.bc2021.p001},
year = {2021},
date = {2021-09-15},
urldate = {2021-09-15},
booktitle = {BC21 : Computational Neuroscience & Neurotechnology Bernstein Conference 2021},
publisher = {BCCN Bernstein Network Computational Network},
keywords = {Machine Learning, Reinforcement learning},
pubstate = {published},
tppubtype = {inproceedings}
}
Aline Xavier Fidencio; Tobias Glasmachers; Christian Klaes; Ioannis Iossifidis
Beyond error correction: Integration of error-related potentials into brain-computer interfaces for improved performance Proceedings Article
In: BC21 : Computational Neuroscience & Neurotechnology Bernstein Conference 2021, BCCN Bernstein Network Computational Network, 2021.
Links | BibTeX | Tags: BCI, Machine Learning
@inproceedings{xavierfidencioErrorCorrectionIntegration2021,
title = {Beyond error correction: Integration of error-related potentials into brain-computer interfaces for improved performance},
author = {Aline Xavier Fidencio and Tobias Glasmachers and Christian Klaes and Ioannis Iossifidis},
doi = {10.12751/nncn.bc2021.p163},
year = {2021},
date = {2021-09-15},
urldate = {2021-09-15},
booktitle = {BC21 : Computational Neuroscience & Neurotechnology Bernstein Conference 2021},
publisher = {BCCN Bernstein Network Computational Network},
keywords = {BCI, Machine Learning},
pubstate = {published},
tppubtype = {inproceedings}
}
Stephan Johann Lehmler; Muhammad Saif-ur-Rehman; Tobias Glasmachers; Ioannis Iossifidis
Transfer-Learning for Patient Specific Model Re-Calibration: Application to sEMG-Classification Proceedings Article
In: BC21 : Computational Neuroscience & Neurotechnology Bernstein Conference 2021, BCCN Bernstein Network Computational Network, 2021.
Links | BibTeX | Tags: BCI, Machine Learning
@inproceedings{lehmlerTransferLearningPatientSpecific2021,
title = {Transfer-Learning for Patient Specific Model Re-Calibration: Application to sEMG-Classification},
author = {Stephan Johann Lehmler and Muhammad Saif-ur-Rehman and Tobias Glasmachers and Ioannis Iossifidis},
doi = {10.12751/nncn.bc2021.p005},
year = {2021},
date = {2021-09-15},
urldate = {2021-09-15},
booktitle = {BC21 : Computational Neuroscience & Neurotechnology Bernstein Conference 2021},
publisher = {BCCN Bernstein Network Computational Network},
keywords = {BCI, Machine Learning},
pubstate = {published},
tppubtype = {inproceedings}
}
Marie Dominique Schmidt; Tobias Glasmachers; Ioannis Iossifidis
Artificially Generated Muscle Signals Proceedings Article
In: BC21 : Computational Neuroscience & Neurotechnology Bernstein Conference 2021, BCCN Bernstein Network Computational Network, 2021.
Links | BibTeX | Tags: BCI, Machine Learning
@inproceedings{schmidtArtificiallyGeneratedMuscle2021,
title = {Artificially Generated Muscle Signals},
author = {Marie Dominique Schmidt and Tobias Glasmachers and Ioannis Iossifidis},
doi = {10.12751/nncn.bc2021.p111},
year = {2021},
date = {2021-09-15},
urldate = {2021-09-15},
booktitle = {BC21 : Computational Neuroscience & Neurotechnology Bernstein Conference 2021},
publisher = {BCCN Bernstein Network Computational Network},
keywords = {BCI, Machine Learning},
pubstate = {published},
tppubtype = {inproceedings}
}
Tim Sziburis; Susanne Blex; Tobias Glasmachers; Inaki Rano; Ioannis Iossifidis
Modelling the Generation of Human Upper-Limb Reaching Trajectories: An Extended Behavioural Attractor Dynamics Approach Proceedings Article
In: BC21 : Computational Neuroscience & Neurotechnology Bernstein Conference 2021, BCCN Bernstein Network Computational Network, 2021.
Links | BibTeX | Tags: BCI, Machine Learning, movement model
@inproceedings{sziburisModellingGenerationHuman2021,
title = {Modelling the Generation of Human Upper-Limb Reaching Trajectories: An Extended Behavioural Attractor Dynamics Approach},
author = {Tim Sziburis and Susanne Blex and Tobias Glasmachers and Inaki Rano and Ioannis Iossifidis},
doi = {10.12751/nncn.bc2021.p078},
year = {2021},
date = {2021-09-15},
urldate = {2021-09-15},
booktitle = {BC21 : Computational Neuroscience & Neurotechnology Bernstein Conference 2021},
publisher = {BCCN Bernstein Network Computational Network},
keywords = {BCI, Machine Learning, movement model},
pubstate = {published},
tppubtype = {inproceedings}
}
Omair Ali; Muhammad Saif-ur-Rehman; Susanne Dyck; Tobias Glasmachers; Ioannis Iossifidis; Christian Klaes
Anchored-STFT and GNAA: An Extension of STFT in Conjunction with an Adversarial Data Augmentation Technique for the Decoding of Neural Signals Journal Article
In: arXiv:2011.14694 [cs, q-bio], 2021.
Abstract | BibTeX | Tags: BCI, Machine Learning, Quantitative Biology, Quantitative Methods
@article{aliAnchoredSTFTGNAAExtension2021,
title = {Anchored-STFT and GNAA: An Extension of STFT in Conjunction with an Adversarial Data Augmentation Technique for the Decoding of Neural Signals},
author = {Omair Ali and Muhammad Saif-ur-Rehman and Susanne Dyck and Tobias Glasmachers and Ioannis Iossifidis and Christian Klaes},
year = {2021},
date = {2021-08-01},
urldate = {2021-08-01},
journal = {arXiv:2011.14694 [cs, q-bio]},
abstract = {Brain-computer interfaces (BCIs) enable communication between humans and machines by translating brain activity into control commands. Electroencephalography (EEG) signals are one of the most used brain signals in non-invasive BCI applications but are often contaminated with noise. Therefore, it is possible that meaningful patterns for classifying EEG signals are deeply hidden. State-of-the-art deep-learning algorithms are successful in learning hidden, meaningful patterns. However, the quality and the quantity of the presented inputs is pivotal. Here, we propose a novel feature extraction method called anchored Short Time Fourier Transform (anchored-STFT), which is an advanced version of STFT, as it minimizes the trade-off between temporal and spectral resolution presented by STFT. In addition, we propose a novel augmentation method, called gradient norm adversarial augmentation (GNAA). GNAA is not only an augmentation method but is also used to harness adversarial inputs in EEG data, which not only improves the classification accuracy but also enhances the robustness of the classifier. In addition, we also propose a new CNN architecture, namely Skip-Net, for the classification of EEG signals. The proposed pipeline outperforms all state-of-the-art methods and yields an average classification accuracy of 90.7 % and 89.54 % on BCI competition II dataset III and BCI competition IV dataset 2b, respectively.},
keywords = {BCI, Machine Learning, Quantitative Biology, Quantitative Methods},
pubstate = {published},
tppubtype = {article}
}
Brain-computer interfaces (BCIs) enable communication between humans and machines by translating brain activity into control commands. Electroencephalography (EEG) signals are one of the most used brain signals in non-invasive BCI applications but are often contaminated with noise. Therefore, it is possible that meaningful patterns for classifying EEG signals are deeply hidden. State-of-the-art deep-learning algorithms are successful in learning hidden, meaningful patterns. However, the quality and the quantity of the presented inputs is pivotal. Here, we propose a novel feature extraction method called anchored Short Time Fourier Transform (anchored-STFT), which is an advanced version of STFT, as it minimizes the trade-off between temporal and spectral resolution presented by STFT. In addition, we propose a novel augmentation method, called gradient norm adversarial augmentation (GNAA). GNAA is not only an augmentation method but is also used to harness adversarial inputs in EEG data, which not only improves the classification accuracy but also enhances the robustness of the classifier. In addition, we also propose a new CNN architecture, namely Skip-Net, for the classification of EEG signals. The proposed pipeline outperforms all state-of-the-art methods and yields an average classification accuracy of 90.7 % and 89.54 % on BCI competition II dataset III and BCI competition IV dataset 2b, respectively.
A X Fidêncio; T Glasmachers; D Naro
Application of Reinforcement Learning to a Mining System Proceedings Article
In: 2021 IEEE 19th World Symposium on Applied Machine Intelligence and Informatics (SAMI), pp. 000111–000118, 2021.
Abstract | Links | BibTeX | Tags: Control Applications, Industrial Application, Machine Learning, Machine learning algorithms, Mining Industry, Reinforcement learning
@inproceedings{fidencioApplicationReinforcementLearning2021,
title = {Application of Reinforcement Learning to a Mining System},
author = {A X Fidêncio and T Glasmachers and D Naro},
doi = {10.1109/SAMI50585.2021.9378663},
year = {2021},
date = {2021-01-01},
urldate = {2021-01-01},
booktitle = {2021 IEEE 19th World Symposium on Applied Machine Intelligence and Informatics (SAMI)},
pages = {000111–000118},
abstract = {Automation techniques have been widely applied in different industry segments, among others, to increase both productivity and safety. In the mining industry, with the usage of such systems, the operator can be removed from hazardous environments without compromising task execution and it is possible to achieve more efficient and standardized operation. In this work a study case on the application of machine learning algorithms to a mining system example is presented, in which reinforcement learning algorithms were used to solve a control problem. As an example, a machine chain consisting of a Bucket Wheel Excavator, a Belt Wagon and a Hopper Car was used. This system has two material transfer points that need to remain aligned during operation in order to allow continuous material flow. To keep the alignment, the controller makes use of seven degrees of freedom given by slewing, luffing and crawler drives. Experimental tests were done in a simulated environment with two state-of-the-art algorithms, namely Proximal Policy Optimization (PPO) and Soft Actor-Critic (SAC). The trained agents were evaluated in terms of episode return and length, as well as alignment quality and action values used. Results show that, for the given task, the PPO agent performs quantitatively and qualitatively better than the SAC agent. However, none of the agents were able to completely solve the proposed testing task.},
keywords = {Control Applications, Industrial Application, Machine Learning, Machine learning algorithms, Mining Industry, Reinforcement learning},
pubstate = {published},
tppubtype = {inproceedings}
}
Automation techniques have been widely applied in different industry segments, among others, to increase both productivity and safety. In the mining industry, with the usage of such systems, the operator can be removed from hazardous environments without compromising task execution and it is possible to achieve more efficient and standardized operation. In this work a study case on the application of machine learning algorithms to a mining system example is presented, in which reinforcement learning algorithms were used to solve a control problem. As an example, a machine chain consisting of a Bucket Wheel Excavator, a Belt Wagon and a Hopper Car was used. This system has two material transfer points that need to remain aligned during operation in order to allow continuous material flow. To keep the alignment, the controller makes use of seven degrees of freedom given by slewing, luffing and crawler drives. Experimental tests were done in a simulated environment with two state-of-the-art algorithms, namely Proximal Policy Optimization (PPO) and Soft Actor-Critic (SAC). The trained agents were evaluated in terms of episode return and length, as well as alignment quality and action values used. Results show that, for the given task, the PPO agent performs quantitatively and qualitatively better than the SAC agent. However, none of the agents were able to completely solve the proposed testing task.
Stephan Johann Lehmler; Muhammad Saif-ur-Rehman; Tobias Glasmachers; Ioannis Iossifidis
Deep Transfer-Learning for patient specific model re-calibration: Application to sEMG-Classification Journal Article
In: 2021.
Links | BibTeX | Tags: BCI, Computational Complexity, Deep Transfer-Learning, Machine Learning
@article{lehmler2021deep,
title = {Deep Transfer-Learning for patient specific model re-calibration: Application to sEMG-Classification},
author = {Stephan Johann Lehmler and Muhammad Saif-ur-Rehman and Tobias Glasmachers and Ioannis Iossifidis},
url = {https://api.semanticscholar.org/CorpusID:245634948},
year = {2021},
date = {2021-01-01},
urldate = {2021-01-01},
keywords = {BCI, Computational Complexity, Deep Transfer-Learning, Machine Learning},
pubstate = {published},
tppubtype = {article}
}
2020
Muhammad Ayaz Hussain; Muhammad Saif-ur-Rehman; Christian Klaes; Ioannis Iossifidis
Comparison of Anomaly Detection between Statistical Method and Undercomplete Proceedings Article
In: IEEE IInternational Congress on Big Data, pp. 32–38, Los Angeles, USA, 2020.
Links | BibTeX | Tags: Anomaly Detection, Autoencoder, Machine Learning
@inproceedings{Hussain2020,
title = {Comparison of Anomaly Detection between Statistical Method and Undercomplete},
author = {Muhammad Ayaz Hussain and Muhammad Saif-ur-Rehman and Christian Klaes and Ioannis Iossifidis},
doi = {https://doi.org/10.1145/3404687.3404689},
year = {2020},
date = {2020-01-01},
urldate = {2020-01-01},
booktitle = {IEEE IInternational Congress on Big Data},
pages = {32–38},
address = {Los Angeles, USA},
keywords = {Anomaly Detection, Autoencoder, Machine Learning},
pubstate = {published},
tppubtype = {inproceedings}
}
Muhammad Saif-ur-Rehman; Omair Ali; Susanne Dyck; Robin Lienkämper; Marita Metzler; Yaroslav Parpaley; Jörg Wellmer; Charles Liu; Brian Lee; Spencer Kellis; Richard A Andersen; Ioannis Iossifidis; Tobias Glasmachers; Christian Klaes
SpikeDeep-Classifier: A deep-learning based fully automatic offline spike sorting algorithm Journal Article
In: Journal of Neural Engineering, 2020.
Abstract | Links | BibTeX | Tags: BCI, CNN, Machine Learning, Spike Sorting
@article{10.1088/1741-2552/abc8d4,
title = {SpikeDeep-Classifier: A deep-learning based fully automatic offline spike sorting algorithm},
author = {Muhammad Saif-ur-Rehman and Omair Ali and Susanne Dyck and Robin Lienkämper and Marita Metzler and Yaroslav Parpaley and Jörg Wellmer and Charles Liu and Brian Lee and Spencer Kellis and Richard A Andersen and Ioannis Iossifidis and Tobias Glasmachers and Christian Klaes},
url = {http://iopscience.iop.org/article/10.1088/1741-2552/abc8d4},
year = {2020},
date = {2020-01-01},
urldate = {2020-01-01},
journal = {Journal of Neural Engineering},
abstract = {Objective. Advancements in electrode design have resulted in micro-electrode arrays with hundreds of channels for single cell recordings. In the resulting electrophysiological recordings, each implanted electrode can record spike activity (SA) of one or more neurons along with background activity (BA). The aim of this study is to isolate SA of each neural source. This process is called spike sorting or spike classification. Advanced spike sorting algorithms are time consuming because of the human intervention at various stages of the pipeline. Current approaches lack generalization because the values of hyperparameters are not fixed, even for multiple recording sessions of the same subject. In this study, a fully automatic spike sorting algorithm called “SpikeDeep-Classifier” is proposed. The values of hyperparameters remain fixed for all the evaluation data. Approach. The proposed approach is based on our previous study (SpikeDeeptector) and a novel background activity rejector (BAR), which are both supervised learning algorithms and an unsupervised learning algorithm (K-means). SpikeDeeptector and BAR are used to extract meaningful channels and remove BA from the extracted meaningful channels, respectively. The process of clustering becomes straight-forward once the BA is completely removed from the data. Then, K-means with a predefined maximum number of clusters is applied on the remaining data originating from neural sources only. Lastly, a similarity-based criterion and a threshold are used to keep distinct clusters and merge similar looking clusters. The proposed approach is called cluster accept or merge (CAOM) and it has only two hyperparameters (maximum number of clusters and similarity threshold) which are kept fixed for all the evaluation data after tuning. Main Results. We compared the results of our algorithm with ground-truth labels. The algorithm is evaluated on data of human patients and publicly available labeled non-human primates (NHPs) datasets. The average accuracy of BAR on datasets of human patients is 92.3% which is further reduced to 88.03% after (K-means + CAOM). In addition, the average accuracy of BAR on a publicly available labeled dataset of NHPs is 95.40% which reduces to 86.95% after (K-mean + CAOM). Lastly, we compared the performance of the SpikeDeep-Classifier with two human experts, where SpikeDeep-Classifier has produced comparable results. Significance. The results demonstrate that “SpikeDeep-Classifier” possesses the ability to generalize well on a versatile dataset and henceforth provides a generalized well on a versatile dataset and henceforth provides a generalized and fully automated solution to offline spike sorting.},
keywords = {BCI, CNN, Machine Learning, Spike Sorting},
pubstate = {published},
tppubtype = {article}
}
Objective. Advancements in electrode design have resulted in micro-electrode arrays with hundreds of channels for single cell recordings. In the resulting electrophysiological recordings, each implanted electrode can record spike activity (SA) of one or more neurons along with background activity (BA). The aim of this study is to isolate SA of each neural source. This process is called spike sorting or spike classification. Advanced spike sorting algorithms are time consuming because of the human intervention at various stages of the pipeline. Current approaches lack generalization because the values of hyperparameters are not fixed, even for multiple recording sessions of the same subject. In this study, a fully automatic spike sorting algorithm called “SpikeDeep-Classifier” is proposed. The values of hyperparameters remain fixed for all the evaluation data. Approach. The proposed approach is based on our previous study (SpikeDeeptector) and a novel background activity rejector (BAR), which are both supervised learning algorithms and an unsupervised learning algorithm (K-means). SpikeDeeptector and BAR are used to extract meaningful channels and remove BA from the extracted meaningful channels, respectively. The process of clustering becomes straight-forward once the BA is completely removed from the data. Then, K-means with a predefined maximum number of clusters is applied on the remaining data originating from neural sources only. Lastly, a similarity-based criterion and a threshold are used to keep distinct clusters and merge similar looking clusters. The proposed approach is called cluster accept or merge (CAOM) and it has only two hyperparameters (maximum number of clusters and similarity threshold) which are kept fixed for all the evaluation data after tuning. Main Results. We compared the results of our algorithm with ground-truth labels. The algorithm is evaluated on data of human patients and publicly available labeled non-human primates (NHPs) datasets. The average accuracy of BAR on datasets of human patients is 92.3% which is further reduced to 88.03% after (K-means + CAOM). In addition, the average accuracy of BAR on a publicly available labeled dataset of NHPs is 95.40% which reduces to 86.95% after (K-mean + CAOM). Lastly, we compared the performance of the SpikeDeep-Classifier with two human experts, where SpikeDeep-Classifier has produced comparable results. Significance. The results demonstrate that “SpikeDeep-Classifier” possesses the ability to generalize well on a versatile dataset and henceforth provides a generalized well on a versatile dataset and henceforth provides a generalized and fully automated solution to offline spike sorting.
Omair Ali; Muhammad Saif-ur-Rehman; Susanne Dyck; Tobias Glasmachers; Ioannis Iossifidis; Christian Klaes
Improving the performance of EEG decoding using anchored-STFT in conjunction with gradient norm adversarial augmentation Journal Article
In: arXiv preprint arXiv:2011.14694, 2020.
BibTeX | Tags: Adversarial NN, BCI, EEG, Machine Learning
@article{ali2020improving,
title = {Improving the performance of EEG decoding using anchored-STFT in conjunction with gradient norm adversarial augmentation},
author = {Omair Ali and Muhammad Saif-ur-Rehman and Susanne Dyck and Tobias Glasmachers and Ioannis Iossifidis and Christian Klaes},
year = {2020},
date = {2020-01-01},
urldate = {2020-01-01},
journal = {arXiv preprint arXiv:2011.14694},
keywords = {Adversarial NN, BCI, EEG, Machine Learning},
pubstate = {published},
tppubtype = {article}
}
2019
Muhammad Saif-ur-Rehman; Robin Lienkämper; S Dyck; A Rayana; Y Parpaley; J Wllner; C Liu; B Lee; S Kellis; D Manahan-Vaughn; O Güntürkün; R A Andersen; Ioannis Iossifidis; T Galmachers; C Klaes
Universal SpikeDeeptector Miscellaneous
2019.
Abstract | BibTeX | Tags: BCI, CNN, Machine Learning, Spike Detection, Spike Sorting
@misc{ur-reimann2019a,
title = {Universal SpikeDeeptector},
author = {Muhammad Saif-ur-Rehman and Robin Lienkämper and S Dyck and A Rayana and Y Parpaley and J Wllner and C Liu and B Lee and S Kellis and D Manahan-Vaughn and O Güntürkün and R A Andersen and Ioannis Iossifidis and T Galmachers and C Klaes},
year = {2019},
date = {2019-01-01},
urldate = {2019-01-01},
publisher = {SfN 2019},
abstract = {State-of-the-art microelectrode array technology enables simultaneous, large-scale single unit recordings from hundreds of channels. Identification of channels recording neural data as compared to noise is the first step for all further analyses. Automatizing this process aims at minimizing the human involvement and time for manual curation. In our previous study, we introduced the “SpikeDeeptector” (SD), which enables us to automatically detect and track channels containing neural data from different human patients implanted with different types of microelectrodes across different brain areas. SD works on human data and to some extent on the data of non-human primates (NHPs). However, to make SD more versatile we proposed a more generalized method called “Universal SpikeDeeptector (USD)”, which is an extended version of SD. USD intends to detect and track the channels containing neural data recorded from four different species (rats, ravens, NHPs and humans) using different kinds of microelectrodes and different recording sites. To our knowledge, there is no method that can simultaneously detect and track neural data of multiple species. To enable contextual learning, USD constructs a feature vector from a batch of waveforms. The constructed feature vectors are then fed into a deep-learning algorithm, which learns contextualized, temporal and spatial patterns. USD is a supervised learning method. Therefore, it requires labeled data for training. It is mainly trained on data from a single human tetraplegic patient, and a small but equal portion of data from the remaining three species. The trained model is then evaluated on a test dataset collected from several humans, NHPs, rats, and birds. The results show that the USD performed consistently well across data collected from each species.},
keywords = {BCI, CNN, Machine Learning, Spike Detection, Spike Sorting},
pubstate = {published},
tppubtype = {misc}
}
State-of-the-art microelectrode array technology enables simultaneous, large-scale single unit recordings from hundreds of channels. Identification of channels recording neural data as compared to noise is the first step for all further analyses. Automatizing this process aims at minimizing the human involvement and time for manual curation. In our previous study, we introduced the “SpikeDeeptector” (SD), which enables us to automatically detect and track channels containing neural data from different human patients implanted with different types of microelectrodes across different brain areas. SD works on human data and to some extent on the data of non-human primates (NHPs). However, to make SD more versatile we proposed a more generalized method called “Universal SpikeDeeptector (USD)”, which is an extended version of SD. USD intends to detect and track the channels containing neural data recorded from four different species (rats, ravens, NHPs and humans) using different kinds of microelectrodes and different recording sites. To our knowledge, there is no method that can simultaneously detect and track neural data of multiple species. To enable contextual learning, USD constructs a feature vector from a batch of waveforms. The constructed feature vectors are then fed into a deep-learning algorithm, which learns contextualized, temporal and spatial patterns. USD is a supervised learning method. Therefore, it requires labeled data for training. It is mainly trained on data from a single human tetraplegic patient, and a small but equal portion of data from the remaining three species. The trained model is then evaluated on a test dataset collected from several humans, NHPs, rats, and birds. The results show that the USD performed consistently well across data collected from each species.
Muhammad Ayaz Hussain; Muhammad Saif-ur-Rehman; Christian Klaes; Ioannis Iossifidis
Comparison of Anomaly Detection between Statistical Method and Undercomplete Proceedings Article
In: IEEE IInternational Congress on Big Data, Los Angeles, USA, 2019.
BibTeX | Tags: Anomaly Detection, Autoencoder, Machine Learning
@inproceedings{Hussain2019,
title = {Comparison of Anomaly Detection between Statistical Method and Undercomplete},
author = {Muhammad Ayaz Hussain and Muhammad Saif-ur-Rehman and Christian Klaes and Ioannis Iossifidis},
year = {2019},
date = {2019-01-01},
urldate = {2019-01-01},
booktitle = {IEEE IInternational Congress on Big Data},
address = {Los Angeles, USA},
keywords = {Anomaly Detection, Autoencoder, Machine Learning},
pubstate = {published},
tppubtype = {inproceedings}
}
Muhammad Saif-ur-Rehman; Robin Lienkämper; Yaroslav Parpaley; Jörg Wellmer; Charles Liu; Brian Lee; Spencer Kellis; Richard Andersen; Ioannis Iossifidis; Tobias Glasmachers; Christian Klaes
SpikeDeeptector: a deep-learning based method for detection of neural spiking activity Journal Article
In: Journal of Neural Engineering, vol. 16, no. 5, pp. 056003, 2019.
Abstract | Links | BibTeX | Tags: BCI, CNN, Data Reduction, Machine Learning, Spike Sorting
@article{Saif-ur-Rehman2019,
title = {SpikeDeeptector: a deep-learning based method for detection of neural spiking activity},
author = {Muhammad Saif-ur-Rehman and Robin Lienkämper and Yaroslav Parpaley and Jörg Wellmer and Charles Liu and Brian Lee and Spencer Kellis and Richard Andersen and Ioannis Iossifidis and Tobias Glasmachers and Christian Klaes},
url = {https://iopscience.iop.org/article/10.1088/1741-2552/ab1e63/meta},
doi = {10.1088/1741-2552/ab1e63},
year = {2019},
date = {2019-01-01},
urldate = {2019-01-01},
journal = {Journal of Neural Engineering},
volume = {16},
number = {5},
pages = {056003},
abstract = {Objective . In electrophysiology, microelectrodes are the primary source for recording neural data (single unit activity). These microelectrodes can be implanted individually or in the form of arrays containing dozens to hundreds of channels. Recordings of some channels contain neural activity, which are often contaminated with noise. Another fraction of channels does not record any neural data, but only noise. By noise, we mean physiological activities unrelated to spiking, including technical artifacts and neural activities of neurons that are too far away from the electrode to be usefully processed. For further analysis, an automatic identification and continuous tracking of channels containing neural data is of great significance for many applications, e.g. automated selection of neural channels during online and offline spike sorting. Automated spike detection and sorting is also critical for online decoding in brain–computer interface (BCI) applications, in which on...},
keywords = {BCI, CNN, Data Reduction, Machine Learning, Spike Sorting},
pubstate = {published},
tppubtype = {article}
}
Objective . In electrophysiology, microelectrodes are the primary source for recording neural data (single unit activity). These microelectrodes can be implanted individually or in the form of arrays containing dozens to hundreds of channels. Recordings of some channels contain neural activity, which are often contaminated with noise. Another fraction of channels does not record any neural data, but only noise. By noise, we mean physiological activities unrelated to spiking, including technical artifacts and neural activities of neurons that are too far away from the electrode to be usefully processed. For further analysis, an automatic identification and continuous tracking of channels containing neural data is of great significance for many applications, e.g. automated selection of neural channels during online and offline spike sorting. Automated spike detection and sorting is also critical for online decoding in brain–computer interface (BCI) applications, in which on...
2018
Muhammad Ayaz Hussain; Christian Klaes; Ioannis Iossifidis:
Toward a Model of Timed Arm Movement Based on Temporal Tuning of Neurons in Primary Motor (MI) and Posterior Parietal Cortex (PPC) Title Proceedings Article
In: BC18 : Computational Neuroscience & Neurotechnology Bernstein Conference 2018, BCCN, 2018.
Abstract | BibTeX | Tags: BCI, dynamical systems, Machine Learning, movement model
@inproceedings{bccn18,
title = {Toward a Model of Timed Arm Movement Based on Temporal Tuning of Neurons in Primary Motor (MI) and Posterior Parietal Cortex (PPC) Title},
author = {Muhammad Ayaz Hussain and Christian Klaes and Ioannis Iossifidis:},
year = {2018},
date = {2018-01-01},
urldate = {2018-01-01},
booktitle = {BC18 : Computational Neuroscience & Neurotechnology Bernstein Conference 2018},
publisher = {BCCN},
abstract = {To study driver behavior we set up a lab with fixed base driving simulators. In order to compensate for the lack of physical feedback in this scenario, we aimed for another means of increasing the realism of our system. In the following, we propose an efficient method of head tracking and its integration in our driving simulation. Furthermore, we illuminate why this is a promising boost of the subjects immersion in the virtual world. Our idea for increasing the feeling of immersion is to give the subject feedback on head movements relative to the screen. A real driver sometimes moves his head in order to see something better or to look behind an occluding object. In addition to these intentional movements, a study conducted by Zirkovitz and Harris has revealed that drivers involuntarily tilt their heads when they go around corners in order to maximize the use of visual information available in the scene. Our system reflects the visual changes of any head movement and hence gives feedback on both involuntary and intentional motion. If, for example, subjects move to the left, they will see more from the right-hand side of the scene. If, on the other hand, they move upwards, a larger fraction of the engine hood will be visible. The same holds for the rear view mirror},
keywords = {BCI, dynamical systems, Machine Learning, movement model},
pubstate = {published},
tppubtype = {inproceedings}
}
To study driver behavior we set up a lab with fixed base driving simulators. In order to compensate for the lack of physical feedback in this scenario, we aimed for another means of increasing the realism of our system. In the following, we propose an efficient method of head tracking and its integration in our driving simulation. Furthermore, we illuminate why this is a promising boost of the subjects immersion in the virtual world. Our idea for increasing the feeling of immersion is to give the subject feedback on head movements relative to the screen. A real driver sometimes moves his head in order to see something better or to look behind an occluding object. In addition to these intentional movements, a study conducted by Zirkovitz and Harris has revealed that drivers involuntarily tilt their heads when they go around corners in order to maximize the use of visual information available in the scene. Our system reflects the visual changes of any head movement and hence gives feedback on both involuntary and intentional motion. If, for example, subjects move to the left, they will see more from the right-hand side of the scene. If, on the other hand, they move upwards, a larger fraction of the engine hood will be visible. The same holds for the rear view mirror
2012
S Noth; J Edelbrunner; I Iossifidis
An integrated architecture for the development and assessment of ADAS Proceedings Article
In: IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC, 2012, ISBN: 9781467330640.
Abstract | Links | BibTeX | Tags: ADAS, Autonomous Driving, Machine Learning
@inproceedings{Noth2012a,
title = {An integrated architecture for the development and assessment of ADAS},
author = {S Noth and J Edelbrunner and I Iossifidis},
doi = {10.1109/ITSC.2012.6338805},
isbn = {9781467330640},
year = {2012},
date = {2012-01-01},
booktitle = {IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC},
abstract = {Advanced Driver Assistant Systems act, by definition in natural, often poorly structured, environments and are supposed to closely interact with human operators. Both, natural environments as well as human behaviour have no inherent metric and can not be modelled/measured in the classical way physically plausibly behaving systems are described. textcopyright 2012 IEEE.},
keywords = {ADAS, Autonomous Driving, Machine Learning},
pubstate = {published},
tppubtype = {inproceedings}
}
Advanced Driver Assistant Systems act, by definition in natural, often poorly structured, environments and are supposed to closely interact with human operators. Both, natural environments as well as human behaviour have no inherent metric and can not be modelled/measured in the classical way physically plausibly behaving systems are described. textcopyright 2012 IEEE.
Sebastian Noth; Johann Edelbrunner; Ioannis Iossifidis
A Versatile Simulated Reality Framework: From Embedded Components to ADAS Proceedings Article
In: International Conference on Pervasive and Embedded and Communication Systems, 2012, PECCS2012, 2012.
BibTeX | Tags: Autonomous robotics, Machine Learning, simulated reality, Simulation, virtual reality
@inproceedings{Noth2012b,
title = {A Versatile Simulated Reality Framework: From Embedded Components to ADAS},
author = {Sebastian Noth and Johann Edelbrunner and Ioannis Iossifidis},
year = {2012},
date = {2012-01-01},
booktitle = {International Conference on Pervasive and Embedded and Communication Systems, 2012, PECCS2012},
keywords = {Autonomous robotics, Machine Learning, simulated reality, Simulation, virtual reality},
pubstate = {published},
tppubtype = {inproceedings}
}
2011
Ürün Dogan; Johann Edelbrunner; Ioannis Iossifidis
Autonomous driving: A comparison of machine learning techniques by means of the prediction of lane change behavior Proceedings Article
In: 2011 IEEE International Conference on Robotics and Biomimetics, ROBIO 2011, pp. 1837–1843, 2011, ISSN: 01962892.
Abstract | Links | BibTeX | Tags: ADAS, Autonomous Driving, lane change prediction, Machine Learning
@inproceedings{Dogan2011a,
title = {Autonomous driving: A comparison of machine learning techniques by means of the prediction of lane change behavior},
author = {Ürün Dogan and Johann Edelbrunner and Ioannis Iossifidis},
doi = {10.1109/ROBIO.2011.6181557},
issn = {01962892},
year = {2011},
date = {2011-01-01},
booktitle = {2011 IEEE International Conference on Robotics and Biomimetics, ROBIO 2011},
pages = {1837–1843},
abstract = {In the presented work we compare machine learning techniques in the context of lane change behavior performed by humans in a semi-naturalistic simulated environment. We evaluate different learning approaches using differing feature combinations in order to identify appropriate feature, best feature combination, and the most appropriate machine learning technique for the described task. Based on the data acquired from human drivers in the traffic simulator NISYS TRS1, we trained a recurrent neural network, a feed forward neural network and a set of support vector machines. In the followed test drives the system was able to predict lane changes up to 1.5 sec in beforehand.},
keywords = {ADAS, Autonomous Driving, lane change prediction, Machine Learning},
pubstate = {published},
tppubtype = {inproceedings}
}
In the presented work we compare machine learning techniques in the context of lane change behavior performed by humans in a semi-naturalistic simulated environment. We evaluate different learning approaches using differing feature combinations in order to identify appropriate feature, best feature combination, and the most appropriate machine learning technique for the described task. Based on the data acquired from human drivers in the traffic simulator NISYS TRS1, we trained a recurrent neural network, a feed forward neural network and a set of support vector machines. In the followed test drives the system was able to predict lane changes up to 1.5 sec in beforehand.
Urun Dogan; Johann Edelbrunner; Ioannis Iossifidis
Autonomous Driving: A Comparison of Machine Learning Techniques by Measns of the Prediction of Lane Change Behavior Proceedings Article
In: Proc. IEEE/RSJ International Conference on Robotics and Biomimetics (RoBio2011), 2011.
BibTeX | Tags: driver information systems, feed forward neural network, lane change maneuvers, Machine Learning, recurrent neural network, support vector machines, traffic simulator
@inproceedings{Dogan2011b,
title = {Autonomous Driving: A Comparison of Machine Learning Techniques by Measns of the Prediction of Lane Change Behavior},
author = {Urun Dogan and Johann Edelbrunner and Ioannis Iossifidis},
year = {2011},
date = {2011-01-01},
urldate = {2011-01-01},
booktitle = {Proc. IEEE/RSJ International Conference on Robotics and Biomimetics (RoBio2011)},
keywords = {driver information systems, feed forward neural network, lane change maneuvers, Machine Learning, recurrent neural network, support vector machines, traffic simulator},
pubstate = {published},
tppubtype = {inproceedings}
}
Sebastian Noth; Ioannis Iossifidis
Simulated reality environment for development and assessment of cognitive robotic systems Proceedings Article
In: Proc. IEEE/RSJ International Conference on Robotics and Biomimetics (RoBio2011), 2011.
Abstract | BibTeX | Tags: Autonomous robotics, Machine Learning, Simulation, virtual reality
@inproceedings{Noth2011,
title = {Simulated reality environment for development and assessment of cognitive robotic systems},
author = {Sebastian Noth and Ioannis Iossifidis},
year = {2011},
date = {2011-01-01},
urldate = {2011-01-01},
booktitle = {Proc. IEEE/RSJ International Conference on Robotics and Biomimetics (RoBio2011)},
abstract = {Simulated reality environment incorporating humans and physically plausible behaving robots, providing natural interaction channels, with the option to link simulator to real perception and motion, is gaining importance for the development of cognitive, intuitive interacting and collaborating robotic systems.
In the present work we introduce a head tracking system which is utilized to incorporate human ego motion in simulated environment improving immersion in the context of human-robot collaborative tasks.},
keywords = {Autonomous robotics, Machine Learning, Simulation, virtual reality},
pubstate = {published},
tppubtype = {inproceedings}
}
Simulated reality environment incorporating humans and physically plausible behaving robots, providing natural interaction channels, with the option to link simulator to real perception and motion, is gaining importance for the development of cognitive, intuitive interacting and collaborating robotic systems.
In the present work we introduce a head tracking system which is utilized to incorporate human ego motion in simulated environment improving immersion in the context of human-robot collaborative tasks.
In the present work we introduce a head tracking system which is utilized to incorporate human ego motion in simulated environment improving immersion in the context of human-robot collaborative tasks.