Motor Imagery Classification Using Single Channel of EEG in Online Brain-Computer Interface

This paper presents an efficient method with simplified calculations for classifying left and right hand movement imagery using a single channel of EEG to design a brain-computer interface (BCI) system. The proposed method utilizes wavelet transform to decompose EEG signals into multiple frequency bands. The classification task is performed using the support vector machine (SVM) with the radial basis function (RBF) kernel and the K-nearest neighbor (KNN) algorithm. This paper introduces and compares two techniques that are based on the sixth and eighth levels produced from a deconstructed single C3 channel. For feature extraction, statistical information such as variance, standard deviation, and signal power are individually considered. The achieved results indicate high accuracy for left and right-hand movement, with 100% accuracy for left-hand movement and 87.47% accuracy for right-hand movement. These results were accomplished using SVM with the RBF kernel and KNN algorithms, based on power features extracted from the eighth-level EEG signal. Compared to prior methods utilizing single-channel and multi-channel approaches, this method demonstrates superior performance.