Metaheuristic-Guided Neural Architecture Search for Cross-Session EEG Speech Imagery Decoding

Authors

  • Mahmoud Rahimi * Department of Computer Engineering, Sharif University of Technology, Tehran, Iran.
  • Sara Al-Farsi Department of Computer Science and Software Engineering, College of Information Technology, UAE University, Al Ain, United Arab Emirates.

https://doi.org/10.48313/maa.v1i3.101

Abstract

Brain-Computer Interfaces (BCIs) based on Speech Imagery (SI) hold transformative potential for restoring communication in individuals with locked-in syndrome, Amyotrophic Lateral Sclerosis (ALS), and severe dysarthria. However, practical deployment of Speech Imagery BCIs is critically limited by the cross-session transfer problem: Electroencephalography (EEG) signal distributions shift substantially between recording sessions due to electrode repositioning, impedance fluctuations, and cognitive state variations, causing within-session classification accuracies of 60–70% to collapse to 30–40% in cross-session evaluation. Existing deep learning architectures for EEG decoding are hand-designed and optimized for within-session performance, leaving the cross-session generalization problem largely unaddressed. We propose Metaheuristic-guided Neural Architecture Search (NAS) for Speech Imagery (MetaNAS-SI), a novel framework that automatically discovers optimal neural network architectures explicitly optimized for cross-session EEG Speech Imagery decoding. MetaNAS-SI introduces a hybrid evolutionary search combining aging evolution for macro-architecture exploration with Differential Evolution (DE) for micro-architecture optimization, a cross-session fitness function based on Leave-One-Session-Out (LOSO) evaluation, a searchable Session-Invariant Feature Alignment Module (SIFAM) that performs domain adaptation via maximum mean discrepancy within candidate architectures, and an EEG-specific search space incorporating physiologically motivated temporal convolutions, Common Spatial Pattern (CSP)-inspired spatial filters, and multi-scale temporal attention. MetaNAS-SI was evaluated on three Speech Imagery datasets: KaraOne (5-class), the coretto imagined speech dataset (4-class), and an in-house Farsi-Arabic dataset (6-class, 5 sessions per subject). Cross-session accuracy reached 45.8% on KaraOne (vs. 36.4% for the best baseline), 52.3% on Coretto (vs. 43.8%), and 48.1% on the Farsi-Arabic dataset (vs. 35.2%). Discovered architectures were 3–15× smaller than hand-designed alternatives while achieving 18–38% relative improvement in cross-session accuracy. Ablation analysis confirmed the critical contributions of the cross-session fitness function and SIFAM module. Pareto-optimal architecture selection enables deployment-aware trade-offs between accuracy, model size, and inference latency for real-time BCI applications.

Keywords:

Speech imagery, Electroencephalography decoding, Neural architecture search, Metaheuristic optimization, Cross-session transfer, Domain adaptation, Evolutionary optimization

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Published

2024-09-19

Issue

Vol. 1 No. 3 (1): Metaheuristic Algorithms with Applications (MAA)

Section

Articles

How to Cite

Rahimi, M. ., & Al-Farsi, S. . (2024). Metaheuristic-Guided Neural Architecture Search for Cross-Session EEG Speech Imagery Decoding. Metaheuristic Algorithms With Applications, 1(3), 269-292. https://doi.org/10.48313/maa.v1i3.101

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