A dynamic brain network analysis method for emotional arousal

Abstract

The invention discloses a dynamic brain network analysis method aiming at emotional arousal. A sliding time window was used to segment the data, and a brain functional network was established by transferring entropy. The segmented brain networks are chronologically connected into dynamic brain networks for displaying detailed dynamics of subjects during experimental stimuli. To ensure that dynamic ligation is authentic, clustering and surrogate sequence testing and analysis are used. Finally, use the feature-channel specification information from the optimization calculation to optimize the data and evaluate the activity level to make the results clearer. It provides guidance and basis for finding potentially important stimulus fragments. Compared with the traditional static brain network, this method adopts a more reasonable and scientific way to establish the brain function network, which can more carefully observe and analyze the changes of the brain state during the experimental stimulation process, and the proposed features can effectively simplify the network structure and result analysis. .

Description

technical field [0001] The invention belongs to the field of biological signal processing, and relates to a method for analyzing electroencephalographic signals for constructing a dynamic brain network when emotional stimulation of different arousal levels is performed. technical background [0002] Affective computing (AC) is receiving more and more attention, it can help computers identify and analyze human emotions, so as to establish a good human-computer interaction relationship. Affective computing is related to sentiment analysis and emotion recognition of human emotions. Sentiment analysis is an important part, and the brain plays a major role in the generation and expression of human emotions. Electroencephalogram (EEG) is a signal generated by the spontaneous or rhythmic activity of brain nerve groups recorded by electrodes, which reflects the potential changes of nerve cell groups in functional areas of the brain. And EEG signal has the characteristics of good t...

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Problems solved by technology

However, the establishment of traditional brain networks is often static, which is not enough for us to have a clear enough observation of the experimental stimulation process

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