Graph-Embedded Convolutional Neural Network for Image-Based EEG Emotion Recognition

Emotion recognition from electroencephalograph (EEG) signals has long been essential for affective computing. In this article, we evaluate EEG emotion recognition by converting EEG signals from multiple channels into images such that richer spatial information can be considered and the question of E...

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Veröffentlicht in:	IEEE transactions on emerging topics in computing Jg. 10; H. 3; S. 1399 - 1413
Hauptverfasser:	Song, Tengfei, Zheng, Wenming, Liu, Suyuan, Zong, Yuan, Cui, Zhen, Li, Yang
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.07.2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Affective computing Artificial neural networks Brain modeling Data mining EEG emotion recognition EEG image generation Electrodes Electroencephalography Emotion recognition Emotions Feature extraction graph convolutional neural network graph embedded convolutional neural network Image processing Image recognition Neural networks Normal distribution Object recognition Performance evaluation Spatial data
ISSN:	2168-6750, 2168-6750
Online-Zugang:	Volltext
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