Selection of proper frequency band and compatible features for left and right hand movement from EEG signal analysis

Electroencephalogram (EEG) signal plays an important role in the field of brain-computer interface (BCI) which has diverse applications ranging from medicine to entertainment. BCI acquires brain signals, extracts informative features and translates these features into a control signal for an external device. The Purpose of this work is to select proper frequency band and to extract suitable features for left and right hand movements from EEG signals analysis. The Discrete Wavelet Transform (DWT) is used to extract different significant features, which separates Alpha, Beta and Theta band of frequencies of the EEG signal. Extracted EEG features of different bands are classified using an artificial neural network (ANN) trained with the back propagation (BP) algorithm. The classification rate shows that Beta band (97.5%) has higher mapping precision and better convergence rate than the other bands, alpha (93.2%) and theta (87.8%). Finally an ANN self-organizing feature mapping (SOFM) is used to find the compatible feature for EEG bands related to hand movement. SOFM analysis shows that approximate entropy (ApEn) for theta band and scale variance for alpha and beta band can be used as compatible feature. The results of this study are expected to be helpful in brain computer interfacing.