Motor imagery classification based upon wavelet packet decomposition and binary particle swarm optimisation

Motor imagery (MI)-electroencephalogram (EEG) signals are most convenient basis for brain computer interface (BCI) systems. This investigation aims to develop a novel method for feature extraction and selection that improves classification accuracy in the BCI system. Wavelet packet decomposition (WPD) and approximate entropy (ApEn) are used for feature extraction for EEG signals. Binary particle swarm optimisation (BPSO) technique is used for feature selection. Artificial neural network (ANN) is employed for classifying MI activities with mean accuracy of 87.29%. The proposed methodology can be useful in clinical applications for motor-disabled subjects connecting outside environment with sequences of MI activities.