Independent components for EEG signal classification
Tobie Erhard Olivier, Shengzhi Du, Barend Jacobus van Wyk, Yskandar Hamam · 2016
This paper addresses movement imagery detection via electroencephalogram (EEG) signal classification. Independent component analysis (ICA) is employed to factorise the time domain EEG signal. A three-layer Neural Network (NN) frame work is constructed to classify the movement imageries using the power spectrum features of independent components (ICs). The main contributions of the paper are reflected in the following points: (1) unlike existing methods, the ICA is not used to reject artifacts but considered as the source for extracting features to train the Neural Network classifiers; (2) a voting NN classification framework is proposed. The experiment results is based on the data obtained from the 2008 Berlin BCI Competition database and shows the proposed method has a high classification capacity.