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2025
1.
 Fidêncio, Aline Xavier;  Grün, Felix;  Klaes, Christian;  Iossifidis, Ioannis
Hybrid Brain-Computer Interface Using Error-Related Potential and Reinforcement Learning Artikel 
In: Frontiers in Human Neuroscience, Bd. 19, 2025, ISSN: 1662-5161.
Abstract | Links | BibTeX | Schlagwörter: 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}

}

Schließen
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.
Schließen
https://www.frontiersin.org/journals/human-neuroscience/articles/10.3389/fnhum.2[...]
doi:10.3389/fnhum.2025.1569411
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