SAE+LSTM: A New Framework for Emotion Recognition From Multi-Channel EEG

EEG-based automatic emotion recognition can help brain-inspired robots in improving their interactions with humans. This paper presents a novel framework for emotion recognition using multi-channel electroencephalogram (EEG). The framework consists of a linear EEG mixing model and an emotion timing...

Full description

Saved in:

Bibliographic Details
Published in:	Frontiers in neurorobotics Vol. 13; p. 37
Main Authors:	Xing, Xiaofen, Li, Zhenqi, Xu, Tianyuan, Shu, Lin, Hu, Bin, Xu, Xiangmin
Format:	Journal Article
Language:	English
Published:	Switzerland Frontiers Research Foundation 12.06.2019 Frontiers Media S.A
Subjects:	Algorithms Arousal Asymmetry Brain research EEG emotion recognition Emotions International conferences Long short-term memory LSTM Methods neural network Neural networks Noise Physiology Robotics and AI Signal processing Stack AutoEncoder Wavelet transforms China Stack AutoEncoder LSTM emotion recognition neural network EEG
ISSN:	1662-5218, 1662-5218
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Be the first to leave a comment!