Abstract
As an emerging technique, brain-computer interfaces (BCIs) bring us a new communication interface which can translate brain activities into control signals of devices like computers, robots etc. In this study, we introduce an independent component analysis (ICA) based semi-supervised learning algorithm for BCI systems. In this algorithm, we separate the raw electroencephalographic (EEG) signals into several independent components using ICA; then choose a best independent component for feature extraction and classification. To demonstrate the validity of our algorithm, we apply it to an data set from an EEG-based cursor control experiment implemented in Wadsworth Center. The data analysis results show that both ICA preprocessing and semi-supervised learning can improve prediction accuracy significantly.
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Qin, J., Li, Y., Liu, Q. (2006). ICA Based Semi-supervised Learning Algorithm for BCI Systems. In: Rosca, J., Erdogmus, D., Príncipe, J.C., Haykin, S. (eds) Independent Component Analysis and Blind Signal Separation. ICA 2006. Lecture Notes in Computer Science, vol 3889. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11679363_27
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DOI: https://doi.org/10.1007/11679363_27
Publisher Name: Springer, Berlin, Heidelberg
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