EEG channel selection-based binary particle swarm optimization with recurrent convolutional autoencoder for emotion recognition

Electroencephalography (EEG) signals can demonstrate the activities of the human brain and recognize different emotional states. Emotion recognition based on full EEG channels leads to the use of redundant data and increases the hardware complexity. This paper presents a channel selection method bas...

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Bibliographic Details

Published in:	Biomedical signal processing and control Vol. 84; p. 104783
Main Authors:	Kouka, Najwa, Fourati, Rahma, Fdhila, Raja, Siarry, Patrick, Alimi, Adel M.
Format:	Journal Article
Language:	English
Published:	Elsevier Ltd 01.07.2023 Elsevier
Subjects:	Binary particle swarm optimization Channel selection Computer Science Convolutional LSTM Local learning strategy Unsupervised EEG feature learning Binary particle swarm optimization Local learning strategy Channel selection Convolutional LSTM Unsupervised EEG feature learning
ISSN:	1746-8094, 1746-8108
Online Access:	Get full text
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