Description

In this process, we proposed a versatile signal processing and analysis framework for Electroencephalogram (EEG). Within this framework the signals were decomposed into the frequency sub-bands using decomposition method, and then a set of statistical features was extracted from the sub-bands to represent the distribution of wavelet coefficients. Independent components analysis (ICA) and continuous wavelet transform (CWT) is used to extract the features from the decomposed signals, and reduce the dimension of data. Then these features were used as an input to a support vector machine (SVM). The performance of classification process is evaluated by means of the Accuracy, Sensitivity and Specificity.