A Review of Usage Metaheuristic Algorithms in Brain-Computer Interface

Authors

https://doi.org/10.48313/maa.v2i2.29

Abstract

Brain–Computer Interface (BCI) systems translate neural activity into machine-interpretable commands, enabling direct communication between the brain and external devices. However, Electroencephalography (EEG) and  Functional Near-Infrared Spectroscopy (FNIRS) signals used in BCIs are inherently noisy, nonstationary, and high-dimensional, making manual feature engineering and model tuning highly inefficient. Metaheuristic optimization algorithms bio-inspired approaches that simulate natural or social behaviors have emerged as powerful tools for automating these processes. This review provides a comprehensive overview of how metaheuristics such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Differential Evolution (DE), Ant Colony Optimization (ACO), and Grey Wolf Optimizer (GWO) have been applied in EEG and FNIRS-based BCIs for channel selection, feature extraction, and classifier tuning. It also discusses the rise of hybrid EEG–FNIRS systems and the integration of metaheuristics with deep learning, Reinforcement Learning (RL), and transfer learning frameworks to enhance adaptability and cross-session generalization. A dedicated case study highlights the Trees Social Relationship (TSR) algorithm a novel ecology-inspired metaheuristic that balances cooperation and competition among solutions. TSR demonstrates strong potential for feature selection, neural network optimization, and adaptive BCI calibration, outperforming traditional algorithms in convergence speed and stability. Collectively, the review identifies key trends from 2020 to 2025, including hybrid and multi-objective metaheuristics, real-time adaptation, and explainable optimization frameworks. The study concludes that metaheuristics are not merely auxiliary tools but foundational elements in building intelligent, robust, and self-adaptive BCI systems capable of real-world operation.

Keywords:

Brain–computer interface, Electroencephalography, Functional near-infrared spectroscopy, Metaheuristic optimization, Feature selection

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Published

2025-06-15

Issue

Vol. 2 No. 2 (2025): Metaheuristic Algorithms with Applications (MAA)

Section

Articles

How to Cite

Parandavar, Z. . (2025). A Review of Usage Metaheuristic Algorithms in Brain-Computer Interface. Metaheuristic Algorithms With Applications, 2(2), 112-133. https://doi.org/10.48313/maa.v2i2.29

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