EEG channels selection for stroke patients rehabilitation using equilibrium optimizer

• Published in: Journal of intelligent systems Vol. 34; no. 1; pp. 39 - 16
• Main Authors: Al-Betar, Mohammed Azmi, Alyasseri, Zaid Abdi Alkareem, Makhadmeh, Sharif Naser
• Format: Journal Article
• Language: English
• Published: Berlin De Gruyter 04.08.2025; Walter de Gruyter GmbH
• Subjects: Accuracy; Channels; EEG; Electroencephalography; equilibrium optimizer; feature extraction; Human-computer interface; Independent component analysis; K-nearest neighbors algorithm; metahurstic algorithms; Optimization; Optimization techniques; Rehabilitation; Stroke; stroke patients; Wavelet analysis; Wavelet transforms
• ISSN: 2191-026X; 0334-1860; 2191-026X
• DOI: 10.1515/jisys-2024-0252

Stroke ranks as the second leading cause of death worldwide and is a major contributor to disability. Researchers have proposed various applications to assist in the rehabilitation of stroke patients, with brain-computer interfaces (BCIs) utilizing electroencephalograms (EEGs) showing particularly promising outcomes. However, the most challenging aspect of using BCI methods is effectively extracting and selecting the most significant features from the vast amount of EEG data available. This article addresses this problem by presenting an innovative optimization-based approach to the channel selection problem, employing a novel binary equilibrium optimizer (EO) as an optimization technique to identify the most relevant EEG channels. This method significantly enhances the accuracy of stroke patient rehabilitation outcomes while reducing computational complexity, avoiding overfitting, and minimizing user discomfort during clinical use. During the preprocessing step, conventional filters and the AICA-WT (automatic independent component analysis with wavelet transform) denoising technique are employed. Attributes are then computed for the time, entropy, and frequency domains. The EO algorithm is utilized to represent the EEG channel selection problem, transforming the features of each individual EEG into binary values and subsequently applying a k-nearest neighbor classifier technique to determine the accuracy rate. The proposed method demonstrates superior performance, achieving the highest accuracy rate of 99% with the HFD features, compared to the existing methods. In general, the proposed study provides a more reliable strategy by identifying a subject-specific reduced set of relevant electrodes and establishes a new benchmark in the field of stroke rehabilitation, highlighting the quality and potential impact of this work.