Feature Extraction and Classification of EEG in Online Brain-Computer Interface

Abstract

In the study of brain-computer interface(BCI),a novel method of extracting electroencephalography(EEG) features based on discrete wavelet transform(DWT) and autoregressive(AR) model was proposed.First,the EEG signal was decomposed to three levels by Daubechies wavelet function and statistics of wavelet coefficients were computed.Also,the sixth-order AR coefficients of the EEG signal were estimated using Burg's algorithm.Then,the combination features were used as an input vector for neural network(NN) classifier,support vector machine(SVM) classifier,and linear discriminant analysis(LDA) classifier.Performance of this feature extraction method was tested using the data set from BCI 2003 competition.The recognition rate was compared with the best result of the competition and the classification results showed the effectiveness of this algorithm.Moreover,applying this pattern recognition algorithm to online robot control system based on EEG,the average accuracy of 89.5% was obtained.This method provides a new idea for the study of online BCI system.