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Dual tree complex wavelet transform domain based motor imagery movements classification scheme from EEG signals

In this paper, a feature extraction method based on Dual Tree Complex Wavelet Transform (DTCWT) domain has been proposed to classify left and right hand motor imagery movements from electroencephalogram (EEG) signals. After first performing auto-correlation of the EEG signals to reduce noise and enhance the weak brain signals, the EEG signals are decomposed into several bands of real and imaginary coefficients using DTCWT. Then mean absolute deviation has been extracted as features from relevant coefficient bands and from the one way ANOVA analysis, scatter plots, box plots and histograms, this feature has shown to be promising to distinguish various kinds of EEG signals. The total experiment is carried out with the publicly available benchmark BCI-competition 2003 Graz motor imagery data set. Among different types of classifiers used K-nearest neighbor (KNN)-based classifiers have been shown to provide a good mean accuracy of 90.36% which is better than several existing techniques.