Technical Implementation Unit for Instrumentation Development, Indonesian Institute of Sciences, Bandung, Indonesia

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Abstract—Electroencephalogram (EEG) recordings provide an important means of brain-computer communication, but their classification accuracy and transfer rate are limited by unexpected signal variations due to artifacts and noises. In this paper, a nonlinear independent component analysis (NICA) extraction method for brain signal based EEG-P300 are proposed. The performance of the proposed method is investigated through a comparison of well known extraction methods (i.e., AAR, JADE, and SOBI algorithms). Finally, the promising results reported here reflect the considerable potential of EEG for the continuous classification of mental states.

Index Terms—Brain computer interface (BCI), Classification accuracy, Transfer rate, Nonlinear, ICA, Electroencephalogram (EEG).

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Cite: Arjon Turnip and Dwi Esti Kusumandari, "Improvement of BCI Performance Through Nonlinear Independent Component Analysis Extraction," Journal of Computers vol. 9, no. 3, pp. 688-695, 2014.