Identification of motor imagery movements from EEG signals using Dual Tree Complex Wavelet Transform

• Published in: 2015 International Conference on Advances in Computing, Communications and Informatics (ICACCI) pp. 290 - 296
• Main Authors: Bashar, Syed Khairul, Hassan, Ahnaf Rashik, Bhuiyan, Mohammed Imamul Hassan
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.08.2015
• Subjects: Auto correlation; BCI; Discrete wavelet transforms; Dual Tree Complex Wavelet Transform (DTCWT); Electroencephalogram (EEG); Electroencephalography; Energy; Feature extraction; Histograms; KNN classifier; Wavelet analysis
• ISBN: 9781479987900; 1479987905
• DOI: 10.1109/ICACCI.2015.7275623

In this paper, Dual Tree Complex Wavelet Transform (DTCWT) domain based feature extraction method has been proposed to identify left and right hand motor imagery movements from electroencephalogram (EEG) signals. After first performing auto-correlation of the EEG signals to enhance the weak brain signals and reduce noise, the EEG signals are decomposed into several bands of real and imaginary coefficients using DTCWT. The energy of the coefficients from relevant bands have been extracted as features and from the one way ANOVA analysis, scatter plots, box plots and histograms, this features are shown to be promising to distinguish various kinds of EEG signals. Publicly available benchmark BCI-competition 2003 Graz motor imagery dataset is used for this experiment. Among different types of classifiers developed such as support vector machine (SVM), probabilistic neural network (PNN), adaptive neuro fuzzy inference system (ANFIS) and K-nearest neighbor (KNN), KNN classifiers have been shown to provide a good mean accuracy of 91.07% which is better than several existing techniques.