期刊名称：International Journal of Innovative Research in Science, Engineering and Technology
印刷版ISSN：2347-6710
电子版ISSN：2319-8753
出版年度：2017
卷号：6
期号：8
页码：15595
DOI：10.15680/IJIRSET.2017.0608011
出版社：S&S Publications
摘要：In this work, we proposed a Hybrid approach for feature extraction and classification using EEG signal.This method uses the decomposition of signals into the frequency sub bands by wavelet method (DWT) and a set ofstatically features and frequency domain features were extracted from the EEG signals to represent the distribution ofwavelet coefficients in Time domain and frequency domain. Data dimension methods like ICA, PCA and LDA arereviewed and ICA is used for feature extraction the reduction of dimension of data and then these extracted features assignal vector are given input to the classifiers and the performance and accuracy of classifiers like SVM, ANN and k-NN are compared with proposed method and a modified algorithm is developed which is best in terms of accuracy andperformance..
