Badan Riset dan Inovasi Nasional
07-11-2022
12-08-2024
b2134e54-aeea-49ca-923f-ab4d503615be
This paper analyzes some practical and technical issues of task scheduling on pa...
Many tree breeding programs ranging from conventional to molecular genetics appr...
By extracting specific brain activity from recorded EEG signals and linking it t...
Platinum teak wood is a fast growing teak wood which has been developed by Indon...
Analysis of EEG activity usually raises the problem of differentiating between g...
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Improvement of BCI performance through nonlinear independent component analisis extraction
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. Journal of Computer, vol. 9, no. 3, pp. 688-695, March 2014