Exploring EEG-based motor imagery decoding: a dual approach using spatial features and spectro-spatial Deep Learning model IFNet

IntroductionIn recent years, the decoding of motor imagery (MI) from electroencephalography (EEG) signals has become a focus of research for brain-machine interfaces (BMIs) and neurorehabilitation. However, EEG signals present challenges due to their non-stationarity and the substantial presence of...

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Hlavní autoři:	Javier V. Juan, Rubén Martínez, Eduardo Iáñez, Mario Ortiz, Jesús Tornero, José M. Azorín
Médium:	Artigo
Jazyk:	Inglês
Vydáno:	Frontiers Media S.A. 2024-02-01
Edice:	Frontiers in Neuroinformatics
Témata:	brain-machine interface (BMI) electroencephalography (EEG) motor imagery (MI) deep learning (DL) convolutional neural network (CNN) common spatial patterns filter bank (CSPFB)
On-line přístup:	https://www.frontiersin.org/articles/10.3389/fninf.2024.1345425/full
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