An integrated deep learning model for motor intention recognition of multi-class EEG Signals in upper limb amputees

•Proposed an integrated deep learning model for recognition of multiple classes of motor intention tasks from raw EEG signals acquired from trans-humeral amputees without any pre-feature extraction.•Pre-processed raw EEG signals in two stages; to remove artifacts and other related noises, and to sel...

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Vydáno v:	Computer methods and programs in biomedicine Ročník 206; s. 106121
Hlavní autoři:	Idowu, Oluwagbenga Paul, Ilesanmi, Ademola Enitan, Li, Xiangxin, Samuel, Oluwarotimi Williams, Fang, Peng, Li, Guanglin
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Ireland Elsevier B.V 01.07.2021
Témata:	Brain computer interface (BCI) Electroencephalography (EEG) Long short-term memory (LSTM) Motor intention (MI) Stacked Autoencoder (SAE) t-distributed stochastic neighbor embedding (t-SNE) t-distributed stochastic neighbor embedding (t-SNE) Electroencephalography (EEG) Stacked Autoencoder (SAE) Long short-term memory (LSTM) Brain computer interface (BCI) Motor intention (MI)
ISSN:	0169-2607, 1872-7565, 1872-7565
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