EEG Based Control - A Study Using Wavelet Features

Abstract

EEG based brain control techniques serves as a strong aid for severely disabled people, as it gives the direct measure of the cortical activity of brain. The work aims at analyzing and classifying the eye blinks obtained from EEG signals for control applications. A wireless headset consisting of 14 terminals was used for acquiring EEG data from 10 healthy subjects. In order to improve the signal quality in raw EEG signals, pre-processing techniques for removing noise and baseline variations were applied. Further Discrete wavelet transform (DWT) was used for extracting required features. Features in wavelet domain: wavelet entropy, wavelet cepstrum and statistical parameters from the approximation coefficients were used for supervised learning and classification. The analysis was carried out for three levels of decomposition using Daubechies 6 wavelet (db6). The system performance was evaluated using the K-Nearest Neighbor and Artificial Neural Networks using the measures: accuracy, sensitivity and specificity.