Attention Induced Dual Convolutional-Capsule Network (AIDC-CN): A deep learning framework for motor imagery classification

In recent times, Electroencephalography (EEG)-based motor imagery (MI) decoding has garnered significant attention due to its extensive applicability in healthcare, including areas such as assistive robotics and rehabilitation engineering. Nevertheless, the decoding of EEG signals presents considera...

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Veröffentlicht in:	Computers in biology and medicine Jg. 183; S. 109260
Hauptverfasser:	Chowdhury, Ritesh Sur, Bose, Shirsha, Ghosh, Sayantani, Konar, Amit
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States Elsevier Ltd 01.12.2024 Elsevier Limited
Schlagworte:	Algorithms Attention Attention - physiology Brain Brain - physiology Brain-Computer Interfaces Capsule network Classification Convolution Decoding Deep Learning EEG Electroencephalography Electroencephalography (EEG) Electroencephalography - methods Error analysis Feature extraction Humans Imagery Imagination - physiology Internal Medicine Machine learning Mental task performance Modules Motor imagery Motor skill learning Motor task performance Neural networks Neural Networks, Computer Other Robotics Signal Processing, Computer-Assisted Signal to noise ratio Deep learning Motor imagery Electroencephalography (EEG) Capsule network
ISSN:	0010-4825, 1879-0534, 1879-0534
Online-Zugang:	Volltext
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