Compact convolutional transformer for subject-independent motor imagery EEG-based BCIs

Keutayeva A. Fakhrutdinov N. Abibullaev B.
December 2024 Nature Research

Scientific Reports
2024 #14 Issue 1

Motor imagery electroencephalography (EEG) analysis is crucial for the development of effective brain-computer interfaces (BCIs), yet it presents considerable challenges due to the complexity of the data and inter-subject variability. This paper introduces EEGCCT, an application of compact convolutional transformers designed specifically to improve the analysis of motor imagery tasks in EEG. Unlike traditional approaches, EEGCCT model significantly enhances generalization from limited data, effectively addressing a common limitation in EEG datasets. We validate and test our models using the open-source BCI Competition IV datasets 2a and 2b, employing a Leave-One-Subject-Out (LOSO) strategy to ensure subject-independent performance. Our findings demonstrate that EEGCCT not only outperforms conventional models like EEGNet in standard evaluations but also achieves better performance compared to other advanced models such as Conformer, Hybrid s-CViT, and Hybrid t-CViT, while utilizing fewer parameters and achieving an accuracy of 70.12%. Additionally, the paper presents a comprehensive ablation study that includes targeted data augmentation, hyperparameter optimization, and architectural improvements.

Brain–computer interface , Compact convolutional transformers , Deep learning , EEG , Motor imagery , Neural signal processing

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Institute of Smart Systems and Artificial Intelligence (ISSAI), Nazarbayev University, Astana, 010000, Kazakhstan
Department of Computer Science, Nazarbayev University, Astana, 010000, Kazakhstan
Department of Robotics Engineering, Nazarbayev University, Astana, 010000, Kazakhstan

Institute of Smart Systems and Artificial Intelligence (ISSAI)
Department of Computer Science
Department of Robotics Engineering

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