Abstract
In this paper a new adaptive Brain Computer Interface (BCI) architecture is proposed that allows to autonomously adapt the BCI parameters in malfunctioning situations. Such situations are detected by discriminating EEG Error Potentials and when necessary the BCI mode is switched back to the training stage in order to improve its performance. First, the modules of the adaptive BCI are presented, then the scenarios for identification of the user reaction to intentionally introduced errors are discussed and finally promising preliminary results are commented. The proposed concept has the potential to increase the reliability of BCI systems.
Cite
CITATION STYLE
Figueiredo, N., Silva, F., Georgieva, P., Milanova, M., & Mendi, E. (2015). Towards an adaptive brain-computer interface – An error potential approach. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8869, pp. 123–129). Springer Verlag. https://doi.org/10.1007/978-3-319-14899-1_12
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