EEG-TCNTransformer: A Temporal Convolutional Transformer for Motor Imagery Brain–Computer Interfaces

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...

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Bibliographic Details

Published in:	Signals
Main Authors:	Anh Hoang Phuc Nguyen, Oluwabunmi Oyefisayo, Maximilian Achim Pfeffer, Sai Ho Ling
Format:	Article
Language:	English
Published:	MDPI AG 2024-09-01
Subjects:	brain–computer interface motor imagery electroencephalography convolutional neural network transformer self-attention
Online Access:	https://www.mdpi.com/2624-6120/5/3/34