EEGFuseNet: Hybrid Unsupervised Deep Feature Characterization and Fusion for High-Dimensional EEG With an Application to Emotion Recognition

How to effectively and efficiently extract valid and reliable features from high-dimensional electroencephalography (EEG), particularly how to fuse the spatial and temporal dynamic brain information into a better feature representation, is a critical issue in brain data analysis. Most current EEG st...

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Bibliographic Details
Published in:	IEEE transactions on neural systems and rehabilitation engineering Vol. 29; pp. 1913 - 1925
Main Authors:	Liang, Zhen, Zhou, Rushuang, Zhang, Li, Li, Linling, Huang, Gan, Zhang, Zhiguo, Ishii, Shin
Format:	Journal Article
Language:	English
Published:	New York IEEE 2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Brain Brain modeling CNN-RNN-GAN Data analysis Decoding EEG Electrodes Electroencephalography Emotion recognition Emotions Feature extraction Fuses Generative adversarial networks hybrid deep encoder-decoder network information fusion Representations unsupervised
ISSN:	1534-4320, 1558-0210, 1558-0210
Online Access:	Get full text
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