Feature selection for neutral vector in EEG signal classification

In the design of brain-computer interface systems, classification of Electroencephalogram (EEG) signals is the essential part and a challenging task. Recently, as the marginalized discrete wavelet transform (mDWT) representations can reveal features related to the transient nature of the EEG signals, the mDWT coefficients have been frequently used in EEG signal classification. In our previous work, we have proposed a super-Dirichlet distribution-based classifier. The proposed classifier performed better than the state-of-the-art support vector machine-based classifier. In this paper, we further study the neutrality of the mDWT coefficients. The mDWT coefficients have unit L1-norm and all the elements are nonnegative. Assuming the mDWT vector coefficients to be a neutral vector, we apply the proposed parallel nonlinear transformation (PNT) framework to transform them non-linearly into a set of independent scalar coefficients. Based on these scalar coefficients, feature selection strategy is proposed on the transformed feature domain. Experimental results show that the feature selection strategy helps improving the classification accuracy.