Improving multi-class EEG-motor imagery classification using two-stage detection on one-versus-one approach

The multi-class motor imagery based on Electroencephalogram (EEG) signals in Brain-Computer Interface (BCI) systems still face challenges, such as inconsistent accuracy and low classification performance due to inter-subject dependent. Therefore, this study aims to improve multi-class EEG-motor imagery using two-stage detection and voting scheme on one-versus-one approach. The EEG signal used to carry out this research was extracted through a statistical measure of narrow window sliding. Furthermore, inter and cross-subject schemes were investigated on BCI competition IV-Dataset 2a to evaluate the effectiveness of the proposed method. The experimental results showed that the proposed method produced enhanced inter and cross-subject kappa coefficient values of 0.78 and 0.68, respectively, with a low standard deviation of 0.1 for both schemes. These results further indicated that the proposed method has an ability to address inter-subject dependent for promising and reliable BCI systems.