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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Veröffentlicht in:	Biomedical signal processing and control Jg. 84; S. 104783
Hauptverfasser:	Kouka, Najwa, Fourati, Rahma, Fdhila, Raja, Siarry, Patrick, Alimi, Adel M.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier Ltd 01.07.2023 Elsevier
Schlagworte:	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-Zugang:	Volltext
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