Analysis of Psychological and Emotional Tendency Based on Brain Functional Imaging and Deep Learning

When facing various pressures, human beings will have different degrees of bad psychological emotions, especially depression and anxiety. How to effectively obtain psychological data signals and use advanced intelligent technology to identify and make decisions is a research hotspot in psychology an...

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Autor principal:	Lin Zhou
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi Limited 2021
Materias:	Mathematics QA1-939
Acceso en línea:	https://doaj.org/article/8ec919e547e3437fb1a055ded12b290f
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id	oai:doaj.org-article:8ec919e547e3437fb1a055ded12b290f
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spelling	oai:doaj.org-article:8ec919e547e3437fb1a055ded12b290f2021-11-29T00:56:13ZAnalysis of Psychological and Emotional Tendency Based on Brain Functional Imaging and Deep Learning1607-887X10.1155/2021/1272502https://doaj.org/article/8ec919e547e3437fb1a055ded12b290f2021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/1272502https://doaj.org/toc/1607-887XWhen facing various pressures, human beings will have different degrees of bad psychological emotions, especially depression and anxiety. How to effectively obtain psychological data signals and use advanced intelligent technology to identify and make decisions is a research hotspot in psychology and computer science. Therefore, a personal emotional tendency analysis method based on brain functional imaging and deep learning is proposed. Firstly, the EEG forward model is established according to functional magnetic resonance imaging (fMRI), and the transfer matrix from the signal source at the cerebral cortex to the head surface electrode is obtained. Therefore, the activation results of fMRI emotional experiment can be mapped to the three-layer head model to obtain the EEG topographic map reflecting the degree of emotional correlation. Then, combining data enhancement (Mixup) with three-dimensional convolutional neural network (3D-CNN), an emotion-related EEG topographic map classification method based on M-3DCNN is proposed. Mixup is used to generate virtual data, the original data and virtual data are used to train the network together, the number of training samples is expanded, the overfitting phenomenon of 3D-CNN is alleviated, and 3D-CNN is used for feature extraction and classification. Experimental data analysis shows that, compared with traditional methods, the proposed method can retain emotion related EEG signals to a greater extent and obtain a higher accuracy of emotion five classifications under the same feature dimension.Lin ZhouHindawi LimitedarticleMathematicsQA1-939ENDiscrete Dynamics in Nature and Society, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Mathematics QA1-939
spellingShingle	Mathematics QA1-939 Lin Zhou Analysis of Psychological and Emotional Tendency Based on Brain Functional Imaging and Deep Learning
description	When facing various pressures, human beings will have different degrees of bad psychological emotions, especially depression and anxiety. How to effectively obtain psychological data signals and use advanced intelligent technology to identify and make decisions is a research hotspot in psychology and computer science. Therefore, a personal emotional tendency analysis method based on brain functional imaging and deep learning is proposed. Firstly, the EEG forward model is established according to functional magnetic resonance imaging (fMRI), and the transfer matrix from the signal source at the cerebral cortex to the head surface electrode is obtained. Therefore, the activation results of fMRI emotional experiment can be mapped to the three-layer head model to obtain the EEG topographic map reflecting the degree of emotional correlation. Then, combining data enhancement (Mixup) with three-dimensional convolutional neural network (3D-CNN), an emotion-related EEG topographic map classification method based on M-3DCNN is proposed. Mixup is used to generate virtual data, the original data and virtual data are used to train the network together, the number of training samples is expanded, the overfitting phenomenon of 3D-CNN is alleviated, and 3D-CNN is used for feature extraction and classification. Experimental data analysis shows that, compared with traditional methods, the proposed method can retain emotion related EEG signals to a greater extent and obtain a higher accuracy of emotion five classifications under the same feature dimension.
format	article
author	Lin Zhou
author_facet	Lin Zhou
author_sort	Lin Zhou
title	Analysis of Psychological and Emotional Tendency Based on Brain Functional Imaging and Deep Learning
title_short	Analysis of Psychological and Emotional Tendency Based on Brain Functional Imaging and Deep Learning
title_full	Analysis of Psychological and Emotional Tendency Based on Brain Functional Imaging and Deep Learning
title_fullStr	Analysis of Psychological and Emotional Tendency Based on Brain Functional Imaging and Deep Learning
title_full_unstemmed	Analysis of Psychological and Emotional Tendency Based on Brain Functional Imaging and Deep Learning
title_sort	analysis of psychological and emotional tendency based on brain functional imaging and deep learning
publisher	Hindawi Limited
publishDate	2021
url	https://doaj.org/article/8ec919e547e3437fb1a055ded12b290f
work_keys_str_mv	AT linzhou analysisofpsychologicalandemotionaltendencybasedonbrainfunctionalimaginganddeeplearning
_version_	1718407720184315904

Analysis of Psychological and Emotional Tendency Based on Brain Functional Imaging and Deep Learning

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