Channel and Feature Selection for a Motor Imagery-Based BCI System Using Multilevel Particle Swarm Optimization

Abstract

Brain-computer interface (BCI) is a communication and control system linking the human brain and computers or other electronic devices. However, irrelevant channels and misleading features unrelated to tasks limit classification performance. To address these problems, we propose an efficient signal processing framework based on particle swarm optimization (PSO) for channel and feature selection, channel selection, and feature selection. Modified Stockwell transforms were used for a feature extraction, and multilevel hybrid PSO-Bayesian linear discriminant analysis was applied to optimization and classification. The BCI Competition III dataset I was used here to confirm the superiority of the proposed scheme. Compared to a method without optimization (89% accuracy), the best classification accuracy of the PSO-based scheme was 99% when less than 10.5% of the original features were used, the test time was reduced by more than 90%, and it achieved Kappa values and F-score of 0.98 and 98.99%, respectively, and better signal-to-noise ratio, thereby outperforming existing algorithms. The results show that the channel and feature selection scheme can accelerate the speed of convergence to the global optimum and reduce the training time. As the proposed framework can significantly improve classification performance, effectively reduce the number of features, and greatly shorten the test time, it can serve as a reference for related real-time BCI application system research.

Domain knowledge-assisted multi-objective evolutionary algorithm for channel selection in brain-computer interface systems

“…As an efficient way to solve NP-compete problems, heuristic-searching-based methods have been adopted to solve channel selection problems successfully. Some commonly used heuristic algorithms are genetic algorithms (Moctezuma and Molinas, 2020 ), particle swarm optimization (Qi et al, 2020 ), simulated annealing (Yang, 2020 ), ant colony optimization (Miao et al, 2020 ), differential evolution (Hajizamani et al, 2020 ), and so on.…”

OptEF-BCI: An Optimization-Based Hybrid EEG and fNIRS–Brain Computer Interface

“…According to the literature [31,32], selecting the right activity channels can help to improve the categorization ability. In addition, several studies have demonstrated the effectiveness of feature selection for BCIs [33][34][35]. Feature selection helps to reduce the dimensionality of the dataset, increase processing efficiency, and improve the classification accuracy.…”

Improvement of the Classification Accuracy of Steady-State Visual Evoked Potential-Based Brain-Computer Interfaces by Combining L1-MCCA with SVM

“…In this study, a means that combining L1-MCCA with particle swarm optimization (PSO) [36][37][38] optimized SVM was proposed to improve the accuracy of SSVEP based BCI. SSVEP data of 15 participants were recorded to investigate the reliability and the performances of the L1-MCCA-PSO-SVM method.…”