Abstract
In motor imagery based Brain-Computer Interface (MI-BCI), subjects should be trained to learn how to modulate the rhythm of EEG for a long time. In previous works, more researchers focused on adaptive BCI system and a few works studied neurofeedback-based subjects training. To achieve high training performance, system self-adaption and subjects training were considered simultaneously in recent works. In this work, we present a co-adaptive training paradigm which includes subjects training and BCI system training. For subjects training, we present a neurofeedback-based training paradigm applying the strength information of motor imagery. In system training, the classifier model is run-by-run updated by selecting good features from EEG data of several previous runs. The online and offline analysis demonstrate that the proposed training paradigm can achieve a better training performance than normal training paradigm.
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Xia, B., Zhang, Q., Xie, H., Li, S., Li, J., He, L. (2012). A Co-adaptive Training Paradigm for Motor Imagery Based Brain-Computer Interface. In: Wang, J., Yen, G.G., Polycarpou, M.M. (eds) Advances in Neural Networks – ISNN 2012. ISNN 2012. Lecture Notes in Computer Science, vol 7367. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31346-2_49
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DOI: https://doi.org/10.1007/978-3-642-31346-2_49
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