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

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...

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Autores principales: Li, Xiang, Zhao, Zhigang, Song, Dawei, Zhang, Yazhou, Pan, Jingshan, Wu, Lu, Huo, Jidong, Niu, Chunyang, Wang, Di
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7061897/
https://www.ncbi.nlm.nih.gov/pubmed/32194367
http://dx.doi.org/10.3389/fnins.2020.00087
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author Li, Xiang
Zhao, Zhigang
Song, Dawei
Zhang, Yazhou
Pan, Jingshan
Wu, Lu
Huo, Jidong
Niu, Chunyang
Wang, Di
author_facet Li, Xiang
Zhao, Zhigang
Song, Dawei
Zhang, Yazhou
Pan, Jingshan
Wu, Lu
Huo, Jidong
Niu, Chunyang
Wang, Di
author_sort Li, Xiang
collection PubMed
description 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 building robust recognition models. Specifically, we propose to utilize an unsupervised deep generative model (e.g., variational autoencoder) to determine the latent factors from the multichannel EEG. Through a sequence modeling method, we examine the emotion recognition performance based on the learnt latent factors. The performance of the proposed methodology is verified on two public datasets (DEAP and SEED) and compared with traditional matrix factorization-based (ICA) and autoencoder-based approaches. Experimental results demonstrate that autoencoder-like neural networks are suitable for unsupervised EEG modeling, and our proposed emotion recognition framework achieves an inspiring performance. As far as we know, it is the first work that introduces variational autoencoder into multichannel EEG decoding for emotion recognition. We think the approach proposed in this work is not only feasible in emotion recognition but also promising in diagnosing depression, Alzheimer's disease, mild cognitive impairment, etc., whose specific latent processes may be altered or aberrant compared with the normal healthy control.
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spelling pubmed-70618972020-03-19 Latent Factor Decoding of Multi-Channel EEG for Emotion Recognition Through Autoencoder-Like Neural Networks Li, Xiang Zhao, Zhigang Song, Dawei Zhang, Yazhou Pan, Jingshan Wu, Lu Huo, Jidong Niu, Chunyang Wang, Di Front Neurosci Neuroscience 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 building robust recognition models. Specifically, we propose to utilize an unsupervised deep generative model (e.g., variational autoencoder) to determine the latent factors from the multichannel EEG. Through a sequence modeling method, we examine the emotion recognition performance based on the learnt latent factors. The performance of the proposed methodology is verified on two public datasets (DEAP and SEED) and compared with traditional matrix factorization-based (ICA) and autoencoder-based approaches. Experimental results demonstrate that autoencoder-like neural networks are suitable for unsupervised EEG modeling, and our proposed emotion recognition framework achieves an inspiring performance. As far as we know, it is the first work that introduces variational autoencoder into multichannel EEG decoding for emotion recognition. We think the approach proposed in this work is not only feasible in emotion recognition but also promising in diagnosing depression, Alzheimer's disease, mild cognitive impairment, etc., whose specific latent processes may be altered or aberrant compared with the normal healthy control. Frontiers Media S.A. 2020-03-02 /pmc/articles/PMC7061897/ /pubmed/32194367 http://dx.doi.org/10.3389/fnins.2020.00087 Text en Copyright © 2020 Li, Zhao, Song, Zhang, Pan, Wu, Huo, Niu and Wang. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neuroscience
Li, Xiang
Zhao, Zhigang
Song, Dawei
Zhang, Yazhou
Pan, Jingshan
Wu, Lu
Huo, Jidong
Niu, Chunyang
Wang, Di
Latent Factor Decoding of Multi-Channel EEG for Emotion Recognition Through Autoencoder-Like Neural Networks
title Latent Factor Decoding of Multi-Channel EEG for Emotion Recognition Through Autoencoder-Like Neural Networks
title_full Latent Factor Decoding of Multi-Channel EEG for Emotion Recognition Through Autoencoder-Like Neural Networks
title_fullStr Latent Factor Decoding of Multi-Channel EEG for Emotion Recognition Through Autoencoder-Like Neural Networks
title_full_unstemmed Latent Factor Decoding of Multi-Channel EEG for Emotion Recognition Through Autoencoder-Like Neural Networks
title_short Latent Factor Decoding of Multi-Channel EEG for Emotion Recognition Through Autoencoder-Like Neural Networks
title_sort latent factor decoding of multi-channel eeg for emotion recognition through autoencoder-like neural networks
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7061897/
https://www.ncbi.nlm.nih.gov/pubmed/32194367
http://dx.doi.org/10.3389/fnins.2020.00087
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