Motor Imagery Classification using Wavelet-Based Features and Tensorflow

Abstract

This paper proposes a methodology for making a decision on left and right motor imagery using Tensorflow and wavelet-based feature extraction. Wavelet coefficients are extracted by the Haar wavelet transforms from electroencephalogram (EEG) signals in the first step. In the second step, 60 wavelet-based features are extracted by the frequency distribution and the amount of variability in frequency distribution. In the final step, this paper classified left or right motion imagery using these 60 features as inputs to the Tensorflow. The proposed methodology shows that the performance result is 82.14% with 60 features in accuracy rate.