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Temporal convolutional transformer for EEG based motor imagery decoding
Altaheri, Hamdi Karray, Fakhri Karimi, Amir-Hossein
Altaheri, Hamdi
Karray, Fakhri
Karimi, Amir-Hossein
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s41598-025-16219-7.pdf
Adobe PDF, 7.26 MB
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Machine Learning
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Journal article
Date
2025
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English
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Abstract
Brain-computer interfaces (BCIs) based on motor imagery (MI) offer a transformative pathway for rehabilitation, communication, and control by translating imagined movements into actionable commands. However, accurately decoding motor imagery from electroencephalography (EEG) signals remains a significant challenge in BCI research. In this paper, we propose TCFormer, a temporal convolutional Transformer designed to improve the performance of EEG-based motor imagery decoding. TCFormer integrates a multi-kernel convolutional neural network (MK-CNN) for spatial-temporal feature extraction with a Transformer encoder enhanced by grouped query attention to capture global contextual dependencies. A temporal convolutional network (TCN) head follows, utilizing dilated causal convolutions to enable the model to learn long-range temporal patterns and generate final class predictions. The architecture is evaluated on three benchmark motor imagery and motor execution EEG datasets: BCIC IV-2a, BCIC IV-2b, and HGD, achieving average accuracies of 84.79, 87.71, and 96.27%, respectively, outperforming current methods. These results demonstrate the effectiveness of the integrated design in addressing the inherent complexity of EEG signals. The code is publicly available at https://github.com/altaheri/TCFormer.
Citation
H. Altaheri, F. Karray, and A.-H. Karimi, “Temporal convolutional transformer for EEG based motor imagery decoding,” Scientific Reports 2025 15:1, vol. 15, no. 1, pp. 1–19, Sep. 2025, doi: 10.1038/S41598-025-16219-7
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Scientific Reports
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Keywords
Transformers, Convolutional Neural Network, Temporal Convolutional Network, Grouped Query Attention, Brain Signal Decoding, Motor Imagery Classification, Electroencephalography (EEG)
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Springer Nature