Compact and interpretable convolutional neural network architecture for electroencephalogram based motor imagery decoding

Recently, due to the popularity of deep learning, the applicability of deep Neural Networks (DNN) algorithms such as the convolutional neural networks (CNN) has been explored in decoding electroencephalogram (EEG) for Brain-Computer Interface (BCI) applications. This allows decoding of the EEG signa...

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Auteur principal:	Ahmad Izzuddin, Tarmizi
Format:	Thèse
Langue:	anglais
Publié:	2022
Sujets:	TK Electrical engineering. Electronics Nuclear engineering
Accès en ligne:	http://eprints.utm.my/101969/1/TarmiziAhmadIzzuddinPSKE2022.pdf.pdf