A logistic binary Jaya optimization-based channel selection scheme for motor-imagery classification in brain-computer interface

Abstract

BCI systems use motor imagery to allow users to control external devices through their brain activity. They extract neural signals from the brain using a large number of EEG channels. However, high-dimensional data from multichannel BCI systems increases the computational burden, leading to slower processing and higher costs. In this study, we proposed a Logistic S-shaped Binary Jaya Optimization Algorithm (LS-BJOA), which combines a logistic map with the Jaya optimization algorithm to alleviate the computational burden caused by many channels. The logistic map introduces stochasticity to better simulate the chaotic behavior of brain signals and improve predictive accuracy. Our method initializes a set of three electrodes as a candidate solution and subsequently determines the most relevant channels iteratively. We used a bi-objective fitness function to evaluate the significance of the selected channels, which involves maximizing classification accuracy and minimizing the length of the channel subset. Initially, a fifth-order bandpass filter with Independent Component Analysis (ICA) was applied to filter MI signals and artifacts reduction, respectively. The Regularized Common Spatial Pattern (RCSP) was used to extract spatiotemporal features from the selected channels. Finally, the three classifiers: (1) Support Vector Machine (SVM), (2) Naïve Bayes (NB), and (3) Linear Discriminant Analysis (LDA) were used to determine maximum classification accuracy. The experiment is validated on three public EEG datasets (BCI Competition IV- 2008 – IIA, BCI Competition IV- dataset 1, BCI competition III – dataset IVa). Our method achieved superior classification accuracy (83.59%, 82.09%, and 89.02% on datasets 1, 2, and 3 respectively) with fewer channels than baseline methods. Additionally, computation time was significantly reduced without compromising accuracy. Topographical mapping revealed frontal lobe involvement in MI tasks during physical activities. Topographical mapping of the selected channels showed that the frontal lobe executes various MI tasks during physical activities.