Multi-channel EEG signal feature extraction and pattern recognition on horizontal mental imagination task of 1-D cursor movement for brain computer interface

Brain computer interfaces (BCIs), based on multi-channel electroencephalogram (EEG) signal processing convert brain signal activities to machine control commands. It provides new communication way with a computer by extracting electroencephalographic activity. This paper, deals with feature extraction and classification of horizontal mental task pattern on 1-D cursor movement from EEG signals. The hemispherical power changes are computed and compared on alpha & beta frequencies and horizontal cursor control extracted with only mental imagination of cursor movements. In the first stage, features are extracted with the well-known average signal power or power difference (alpha and beta) method. Principal component analysis is used for reducing feature dimensions. All features are classified and the mental task patterns are recognized by three neural network classifiers which learning vector quantization, multilayer neural network and probabilistic neural network due to obtaining acceptable good results and using successfully in pattern recognition via k-fold cross validation technique.