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2026
1.
 Fidêncio, Aline Xavier;  Grün, Felix;  Klaes, Christian;  Iossifidis, Ioannis
Performance Boundaries for~Brain-Computer Interfaces Using Error-Related Potentials and~Reinforcement Learning Proceedings Article 
In:  Nicosia, Giuseppe;  Ojha, Varun;  Giesselbach, Sven;  Pardalos, M. Panos;  Umeton, Renato;  Emanuele, La Malfa;  Gabriele, La Malfa (Hrsg.): Machine Learning, Optimization, and Data Science, S. 335–349, Springer Nature Switzerland, Cham, 2026, ISBN: 978-3-032-21480-5.
Abstract | Links | BibTeX | Schlagwörter: adaptive brain-computer interface, BCI, electroencephalography (EEG), error-related potentials (ErrPs), Machine Learning, motor imagery (MI), reinforcement learning (RL)
@inproceedings{xavierfidencioPerformanceBoundariesBrainComputer2026,

title = {Performance Boundaries for~Brain-Computer Interfaces Using Error-Related Potentials and~Reinforcement Learning},

author = {Aline Xavier Fidêncio and Felix Grün and Christian Klaes and Ioannis Iossifidis},

editor = {Giuseppe Nicosia and Varun Ojha and Sven Giesselbach and M. Panos Pardalos and Renato Umeton and La Malfa Emanuele and La Malfa Gabriele},

doi = {10.1007/978-3-032-21480-5_23},

isbn = {978-3-032-21480-5},

year  = {2026},

date = {2026-06-01},

urldate = {2026-06-01},

booktitle = {Machine Learning, Optimization, and Data Science},

pages = {335–349},

publisher = {Springer Nature Switzerland},

address = {Cham},

abstract = {Non-invasive brain-computer interfaces (BCIs) can improve quality of life for individuals with motor disabilities. However, input data non-stationarity often leads to performance degradation over time. Adaptive BCIs aim to address this challenge. Recent studies have proposed leveraging error-related potentials (ErrPs) - neural signals elicited during self-made or observed errors - as a natural feedback mechanism for closed-loop systems. While these approaches demonstrate potential performance gains, their effectiveness relies heavily on accurate ErrP detection, which remains challenging. In a previous study, we introduced a novel reinforcement learning-based BCI framework incorporating ErrPs as reward signals. We systematically examine how misclassification rates in ErrP detection in terms of false positives (FPs) and false negatives (FNs) influence closed-loop performance. We use both synthetic and real datasets to evaluate two contextual bandit algorithms (LinUCB and NeuralUCB), trained to map motor imagery-related time-frequency modulations to actions in a binary task. Firstly, our findings show that the sensitivity of agents to FPs and FNs depends on baseline accuracies. In some conditions, such as insufficient exploration, false negatives might be more detrimental than false positives, but this needs further investigation. Proper agent parametrization and enough data samples can compensate the negative effects to some extent.},

keywords = {adaptive brain-computer interface, BCI, electroencephalography (EEG), error-related potentials (ErrPs), Machine Learning, motor imagery (MI), reinforcement learning (RL)},

pubstate = {published},

tppubtype = {inproceedings}

}

Schließen
Non-invasive brain-computer interfaces (BCIs) can improve quality of life for individuals with motor disabilities. However, input data non-stationarity often leads to performance degradation over time. Adaptive BCIs aim to address this challenge. Recent studies have proposed leveraging error-related potentials (ErrPs) - neural signals elicited during self-made or observed errors - as a natural feedback mechanism for closed-loop systems. While these approaches demonstrate potential performance gains, their effectiveness relies heavily on accurate ErrP detection, which remains challenging. In a previous study, we introduced a novel reinforcement learning-based BCI framework incorporating ErrPs as reward signals. We systematically examine how misclassification rates in ErrP detection in terms of false positives (FPs) and false negatives (FNs) influence closed-loop performance. We use both synthetic and real datasets to evaluate two contextual bandit algorithms (LinUCB and NeuralUCB), trained to map motor imagery-related time-frequency modulations to actions in a binary task. Firstly, our findings show that the sensitivity of agents to FPs and FNs depends on baseline accuracies. In some conditions, such as insufficient exploration, false negatives might be more detrimental than false positives, but this needs further investigation. Proper agent parametrization and enough data samples can compensate the negative effects to some extent.
Schließen
doi:10.1007/978-3-032-21480-5_23
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