The self-similarity concept is used in chaos theory for extracting features and detecting special mind disorder patterns from the EEG signal such as ADHD and Autism. Based on the results, we conclude that the DWPT-DFA method using the ERD mother wavelets improves significantly the efficiency of the SSVM-GRBF classifier.Ī electroencephalograph (EEG) property is self-similarity, which means one part of an object or a signal is similar to the other parts of the object or signal. Results show that the combination of the DWPT and DFA with the personalize ERD mother wavelet gives the best accuracy of 85.33% with p < 0.001. The ERDs and features are extracted from FC1 and CP6 channels. For the efficiency of the method, nine subjects have participated to record EEG based on the imaginary hand movements. The soft margin support vector machine with the generalized radial basis function (SSVM-GRBF) is employed to classify the DWPT-DFA features. The predefined mother wavelets are db4, db8 and coiflet 4. Also, three predefined mother wavelets are used, and the results are compared with the customized mother wavelet. Herein, A customized mother wavelet utilizing event related desynchronization (ERD) potential patterns are extracted and updated automatically for individual subjects. In wavelet technique, mother wavelets play an important role. Both approaches are known as self-similarity quantifier techniques. In this study, two different techniques -detrended fluctuation analysis (DFA) and discrete wavelet packet transform- are combined (DWPT-DFA) for feature extraction. One critical issue in brain computer interface (BCI) studies is to extract imaginary movement patterns from electroencephalograph (EEG).
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