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.