Feature selection of EEG data with neuro-statistical method

Feature selection (FS) of high dimensional electroencephalographic (EEG) data helps to identify and diagnose the brain conditions easily. Features can be selected with different ways where canonical correlation analysis (CCA) is one of them which are a statistical method. We employed neural network (NN) with CCA for salient features extraction of EEG data, called Neural Canonical Correlation Analysis (NCCA), which exhibits better result than individual CCA or NN. A NN classifier is used to test the classification of the selected features. The NN classifier shows remarkable result in terms of recognition rate.