Abstract
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.
Highlights
Emotion often involves people’s immediate needs and subjective attitude and often has complex interaction with other psychological processes
With the continuous progress of modern neuroscience technology, a series of important achievements have been made in brain cognitive neuroscience by means of Electroencephalography (EEG), functional magnetic resonance imaging, and functional near infrared spectrum instrument [6, 7]. is makes new breakthroughs in the research of cognitive problems such as perception, attention, memory, planning, language, and consciousness at the level of brain nerve. e continuous development of cognitive neuroscience and brain activity measurement technology has gradually established a bridge between the subjective world and the objective world
Aiming at the selection of emotion related channels in EEG, functional magnetic resonance imaging (fMRI) is introduced for auxiliary analysis, and an emotion-related EEG channel selection method based on EEG forward model is proposed, which can use the brain activation information obtained by fMRI to assist EEG channel selection. en, aiming at the problem that the insufficient number of samples will lead to the overfitting phenomenon of the network, this paper combines data enhancement (Mixup) with 3D-convolution neural network (CNN) classification and proposes an emotion-related EEG topographic map classification method based on M-3DCNN
Summary
Received 29 September 2021; Revised 18 October 2021; Accepted 29 October 2021; Published 16 November 2021. Erefore, a personal emotional tendency analysis method based on brain functional imaging and deep learning is proposed. 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. Erefore, 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. En, 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. 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
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