EEG channels selection for stroke patients rehabilitation using equilibrium optimizer

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This study demonstrates improved rehabilitation accuracy in stroke patients using an equilibrium optimizer for EEG channel selection.

The method achieves a high accuracy rate of 99% in enhancing stroke rehabilitation outcomes.
Using the equilibrium optimizer, the study identifies the most relevant EEG channels while minimizing user discomfort.
Data preprocessing includes AICA-WT denoising and computational attributes from time, entropy, and frequency domains.
The approach establishes a new benchmark for subject-specific electrode selection in stroke rehabilitation.

Al-Betar et al. (2025) studied this question.

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View Full Paper

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Authors

This study demonstrates improved rehabilitation accuracy in stroke patients using an equilibrium optimizer for EEG channel selection.

The method achieves a high accuracy rate of 99% in enhancing stroke rehabilitation outcomes.
Using the equilibrium optimizer, the study identifies the most relevant EEG channels while minimizing user discomfort.
Data preprocessing includes AICA-WT denoising and computational attributes from time, entropy, and frequency domains.
The approach establishes a new benchmark for subject-specific electrode selection in stroke rehabilitation.

Al-Betar et al. (2025) studied this question.

View Full Paper

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