Version 1
: Received: 8 August 2024 / Approved: 9 August 2024 / Online: 9 August 2024 (12:15:45 CEST)
How to cite:
Nguyen, A. H. P.; Oyefisayo, O.; Pfeffer, M. A.; Ling, S. H. EEG-TCNTransformer: A Temporal Convolutional Transformer for Motor Imagery Brain-Computer Interfaces. Preprints2024, 2024080676. https://doi.org/10.20944/preprints202408.0676.v1
Nguyen, A. H. P.; Oyefisayo, O.; Pfeffer, M. A.; Ling, S. H. EEG-TCNTransformer: A Temporal Convolutional Transformer for Motor Imagery Brain-Computer Interfaces. Preprints 2024, 2024080676. https://doi.org/10.20944/preprints202408.0676.v1
Nguyen, A. H. P.; Oyefisayo, O.; Pfeffer, M. A.; Ling, S. H. EEG-TCNTransformer: A Temporal Convolutional Transformer for Motor Imagery Brain-Computer Interfaces. Preprints2024, 2024080676. https://doi.org/10.20944/preprints202408.0676.v1
APA Style
Nguyen, A. H. P., Oyefisayo, O., Pfeffer, M. A., & Ling, S. H. (2024). EEG-TCNTransformer: A Temporal Convolutional Transformer for Motor Imagery Brain-Computer Interfaces. Preprints. https://doi.org/10.20944/preprints202408.0676.v1
Chicago/Turabian Style
Nguyen, A. H. P., Maximilian Achim Pfeffer and Sai Ho Ling. 2024 "EEG-TCNTransformer: A Temporal Convolutional Transformer for Motor Imagery Brain-Computer Interfaces" Preprints. https://doi.org/10.20944/preprints202408.0676.v1
Abstract
In Brain-Computer Interface Motor Imagery (BCI-MI) systems, Convolutional Neural Networks (CNNs) have traditionally dominated as the deep learning method of choice, demonstrating significant advancements in state-of-the-art studies. Recently, Transformer models with attention mechanisms have emerged as a sophisticated technique, enhancing the capture of long-term dependencies and intricate feature relationships in BCI-MI. This research investigates the performance of EEG-TCNet and EEG-Conformer models, which are trained and validated using various hyperparameters and bandpass filters during preprocessing to assess improvements in model accuracy. Additionally, this study introduces the EEG-TCNTransformer, a novel model that integrates the convolutional architecture of EEG-TCNet with a series of self-attention blocks employing a multi-head structure. The EEG-TCNTransformer achieves an accuracy of 82.97% without the application of bandpass filtering. The source code of EEG-TCNTransformer is available on GitHub.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
