Osagie, Efosa ORCID: https://orcid.org/0009-0004-3462-7175, Balasundaram, Rebecca, Venkatesan, Ramalingam and Egbe, Uyi-os (2025) Single-Channel EEG Classification for Motor Tasks via Bayesian-Optimized Lightweight 1D CNN. In: 2025 33rd Signal Processing and Communications Applications Conference (SIU), 2025, Sile, Istanbul, Turkiye.

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Abstract

Electroencephalogram (EEG) monitoring enables the capture of brain activity and provides insights into motor tasks with applications in rehabilitation, prosthetic control, and Brain-Computer Interfaces (BCI). Traditional classification approaches often rely on complex architectures, limiting usability in resource-constrained settings. This study introduces a 1-dimensional Convolutional Neural Network (1D CNN) optimised using Gaussian-based Bayesian optimisation for classifying single-channel EEG signals. While existing models are deep, requiring extensive pre-and post-processing, the proposed model achieves a trade-off between computational efficiency and classification accuracy. Trained exclusively on a private dataset of 27 individuals performing motor tasks, the model achieved a promising accuracy of 81.82%, demonstrating its potential for practical deployment. The proposed model also outperformed complex pre-trained architectures such as VGG-16 and GoogLeNet by achieving significantly fewer parameters and lower computational demand, making it particularly suitable for resource-limited environments. As an early investigation, this work explores the feasibility of single-channel approaches using lightweight architectures for real-time EEG classification in resource-constrained environment

Item Type:	Conference or Workshop Item (Paper)
Additional Information:	“© 2025 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.”
Status:	Published
DOI:	10.1109/SIU66497.2025.11111954
Subjects:	A General Works > AS Academies and learned societies (General) Q Science > Q Science (General) Q Science > Q Science (General) > Q325 Machine learning R Medicine > RZ Other systems of medicine
School/Department:	London Campus
URI:	https://ray.yorksj.ac.uk/id/eprint/12515

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