Efficient approach for EEG-based emotion recognition

Identification of human emotion involving electroencephalogram (EEG) signals has become an emerging field in health monitoring application as EEG signals can give us a more diverse insight on emotional states. The aim of this study is to develop an efficient framework based on deep learning concept...

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Bibliographic Details
Published in:	Electronics letters Vol. 56; no. 25; pp. 1361 - 1364
Main Authors:	Şengür, D, Siuly, S
Format:	Journal Article
Language:	English
Published:	The Institution of Engineering and Technology 10.12.2020
Subjects:	automatic identification continuous wavelet deep feature selection deep features deep learning concept delta rhythm diverse insight EEG rhythm images EEG signals EEG‐based emotion recognition efficient features electroencephalogram signals electroencephalography emotion recognition emotional states extracted rhythm signals feature extraction health monitoring application human emotion learning (artificial intelligence) long short term memory method low‐pass filtering medical signal processing neural nets pre‐trained convolutional neural networks model Special Issue: Current Trends in Cognitive Science and Brain Computing Research and Applications wavelet transforms wavelet transforms diverse insight emotional states continuous wavelet electroencephalogram signals pre-trained convolutional neural networks model emotion recognition delta rhythm feature extraction EEG signals low-pass filtering learning (artificial intelligence) deep feature selection electroencephalography efficient features EEG rhythm images deep features health monitoring application medical signal processing long short term memory method deep learning concept EEG-based emotion recognition human emotion extracted rhythm signals automatic identification neural nets
ISSN:	0013-5194, 1350-911X, 1350-911X
Online Access:	Get full text
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