Latent Factor Decoding of Multi-Channel EEG for Emotion Recognition Through Autoencoder-Like Neural Networks

• Published in: Frontiers in neuroscience Vol. 14; p. 87
• Main Authors: Li, Xiang, Zhao, Zhigang, Song, Dawei, Zhang, Yazhou, Pan, Jingshan, Wu, Lu, Huo, Jidong, Niu, Chunyang, Wang, Di
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Research Foundation 02.03.2020; Frontiers Media S.A
• Subjects: Alzheimer's disease; Cognition & reasoning; Cognitive ability; deep learning; EEG; emotion recognition; Emotions; latent factor decoding; Methods; Neural networks; Neurodegenerative diseases; Neuroscience; Signal processing; variational autoencoder; deep learning; latent factor decoding; emotion recognition; variational autoencoder; EEG

Robust cross-subject emotion recognition based on multichannel EEG has always been hard work. In this work, we hypothesize that there exist default brain variables across subjects in emotional processes. Hence, the states of the latent variables that relate to emotional processing must contribute to...