2025
Aline Xavier Fidêncio; Felix Grün; Christian Klaes; Ioannis Iossifidis
Error-Related Potential Driven Reinforcement Learning for Adaptive Brain-Computer Interfaces Journal Article
In: Arxiv, 2025.
Abstract | Links | BibTeX | Tags: BCI, Computer Science - Human-Computer Interaction, Computer Science - Machine Learning, EEG, Quantitative Biology - Neurons and Cognition, Reinforcement learning
@article{fidencioErrorrelatedPotentialDriven2025,
title = {Error-Related Potential Driven Reinforcement Learning for Adaptive Brain-Computer Interfaces},
author = {Aline Xavier Fidêncio and Felix Grün and Christian Klaes and Ioannis Iossifidis},
url = {http://arxiv.org/abs/2502.18594},
doi = {10.48550/arXiv.2502.18594},
year = {2025},
date = {2025-02-25},
urldate = {2025-02-25},
journal = {Arxiv},
abstract = {Brain-computer interfaces (BCIs) provide alternative communication methods for individuals with motor disabilities by allowing control and interaction with external devices. Non-invasive BCIs, especially those using electroencephalography (EEG), are practical and safe for various applications. However, their performance is often hindered by EEG non-stationarities, caused by changing mental states or device characteristics like electrode impedance. This challenge has spurred research into adaptive BCIs that can handle such variations. In recent years, interest has grown in using error-related potentials (ErrPs) to enhance BCI performance. ErrPs, neural responses to errors, can be detected non-invasively and have been integrated into different BCI paradigms to improve performance through error correction or adaptation. This research introduces a novel adaptive ErrP-based BCI approach using reinforcement learning (RL). We demonstrate the feasibility of an RL-driven adaptive framework incorporating ErrPs and motor imagery. Utilizing two RL agents, the framework adapts dynamically to EEG non-stationarities. Validation was conducted using a publicly available motor imagery dataset and a fast-paced game designed to boost user engagement. Results show the framework's promise, with RL agents learning control policies from user interactions and achieving robust performance across datasets. However, a critical insight from the game-based protocol revealed that motor imagery in a high-speed interaction paradigm was largely ineffective for participants, highlighting task design limitations in real-time BCI applications. These findings underscore the potential of RL for adaptive BCIs while pointing out practical constraints related to task complexity and user responsiveness.},
keywords = {BCI, Computer Science - Human-Computer Interaction, Computer Science - Machine Learning, EEG, Quantitative Biology - Neurons and Cognition, Reinforcement learning},
pubstate = {published},
tppubtype = {article}
}
Brain-computer interfaces (BCIs) provide alternative communication methods for individuals with motor disabilities by allowing control and interaction with external devices. Non-invasive BCIs, especially those using electroencephalography (EEG), are practical and safe for various applications. However, their performance is often hindered by EEG non-stationarities, caused by changing mental states or device characteristics like electrode impedance. This challenge has spurred research into adaptive BCIs that can handle such variations. In recent years, interest has grown in using error-related potentials (ErrPs) to enhance BCI performance. ErrPs, neural responses to errors, can be detected non-invasively and have been integrated into different BCI paradigms to improve performance through error correction or adaptation. This research introduces a novel adaptive ErrP-based BCI approach using reinforcement learning (RL). We demonstrate the feasibility of an RL-driven adaptive framework incorporating ErrPs and motor imagery. Utilizing two RL agents, the framework adapts dynamically to EEG non-stationarities. Validation was conducted using a publicly available motor imagery dataset and a fast-paced game designed to boost user engagement. Results show the framework's promise, with RL agents learning control policies from user interactions and achieving robust performance across datasets. However, a critical insight from the game-based protocol revealed that motor imagery in a high-speed interaction paradigm was largely ineffective for participants, highlighting task design limitations in real-time BCI applications. These findings underscore the potential of RL for adaptive BCIs while pointing out practical constraints related to task complexity and user responsiveness.
2024
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
Muhammad Ayaz Hussain; Ioannis Iossifidis
Advancements in Upper Body Exoskeleton: Implementing Active Gravity Compensation with a Feedforward Controller Journal Article
In: arXiv:2309.04698 [cs.RO], 2023.
Abstract | Links | BibTeX | Tags: Computer Science - Artificial Intelligence, Computer Science - Information Theory, Computer Science - Machine Learning, I.2.6, I.2.8, Quantitative Biology - Neurons and Cognition
@article{ayazhussainAdvancementsUpperBody2023,
title = {Advancements in Upper Body Exoskeleton: Implementing Active Gravity Compensation with a Feedforward Controller},
author = {Muhammad Ayaz Hussain and Ioannis Iossifidis},
url = {https://doi.org/10.48550/arXiv.2309.04698},
doi = {10.48550/arXiv.2309.04698},
year = {2023},
date = {2023-09-09},
urldate = {2023-09-09},
journal = {arXiv:2309.04698 [cs.RO]},
abstract = {In this study, we present a feedforward control system designed for active gravity compensation on an upper body exoskeleton. The system utilizes only positional data from internal motor sensors to calculate torque, employing analytical control equations based on Newton-Euler Inverse Dynamics. Compared to feedback control systems, the feedforward approach offers several advantages. It eliminates the need for external torque sensors, resulting in reduced hardware complexity and weight. Moreover, the feedforward control exhibits a more proactive response, leading to enhanced performance. The exoskeleton used in the experiments is lightweight and comprises 4 Degrees of Freedom, closely mimicking human upper body kinematics and three-dimensional range of motion. We conducted tests on both hardware and simulations of the exoskeleton, demonstrating stable performance. The system maintained its position over an extended period, exhibiting minimal friction and avoiding undesired slewing.},
keywords = {Computer Science - Artificial Intelligence, Computer Science - Information Theory, Computer Science - Machine Learning, I.2.6, I.2.8, Quantitative Biology - Neurons and Cognition},
pubstate = {published},
tppubtype = {article}
}
In this study, we present a feedforward control system designed for active gravity compensation on an upper body exoskeleton. The system utilizes only positional data from internal motor sensors to calculate torque, employing analytical control equations based on Newton-Euler Inverse Dynamics. Compared to feedback control systems, the feedforward approach offers several advantages. It eliminates the need for external torque sensors, resulting in reduced hardware complexity and weight. Moreover, the feedforward control exhibits a more proactive response, leading to enhanced performance. The exoskeleton used in the experiments is lightweight and comprises 4 Degrees of Freedom, closely mimicking human upper body kinematics and three-dimensional range of motion. We conducted tests on both hardware and simulations of the exoskeleton, demonstrating stable performance. The system maintained its position over an extended period, exhibiting minimal friction and avoiding undesired slewing.