Abstract
In this paper, three different methods for brain signal acquisition are presented. All methods deal with feature extraction method of Electroencephalogram (EEG) based P300 waves. The performance of the three methods is investigated through backpropagation neural network 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. Advanced Science Letters, Volume 20, Number 1, January 2014 , pp. 80-85(6)
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