Enhanced Eeg Signal Classification for Motor Imagery Based Brain Computer Interface: A Fusion Approach Using Discrete Wavelet Transform and Quad Binary Pattern

Abstract

Motor Imagery - Brain-Computer Interface (MI-BCI) is a technique that acts as a non-muscular channel for disabled people to communicate. Because it is a communication link between the wired brain and an external device. In this paper, we propose a comprehensive feature extraction approach for electroencephalogram (EEG) signals to achieve maximum classification accuracy by fusion techniques. The Wavelet Packet Decomposition (WPD), Discrete Wavelet Transform (DWT), and Quad Binary Pattern (QBP) methods are applied to EEG signals. The proposed method consists of five strategies out of which two are fusion techniques based on EEG signal and pattern techniques. One hybrid method involves decomposing EEG signals using DWT, and feeding it into the QBP domain to extract the histogram features. In another hybrid model, the signals are decomposed using WPD and were given to the QBP for feature extraction. Artificial Neural Network (ANN) is used as a classifier in our work. Among the used algorithm DWT based QBP approach attained maximum accuracy. The proposed method is validated using well-known Physionet EEG dataset by considering binary class (imaginary movement of left and right fist). The extracted features are to classify the movement of the right and left fist EEG signals. Using a DWT-based QBP approach, the proposed technique attained a accuracy of 99.50 percent on physionet dataset.