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P300 detection using nonlinear independent component analysis

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Abstract

In this paper, three different methods for brain signal based electroencephalogram (EEG)-P300 extraction are proposed. The performance of the three methods is investigated through Linear Discriminant Analysis classifier. EEG-P300 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. A comparison of extraction methods (i.e., AAR, JADE, and SOBI) entailing time-series EEG signals is proposed. Finally, the promising results reported here reflect the considerable potential of EEG for the continuous classification of mental states.

Telah di Publikasi di
The International Conference on Radar, Antenna, Microwave, Electronics and Telecomunications (ICRAMET), Surabaya March 27-28 2013.

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