Cross subject personality assessment based on electroencephalogram functional connectivity and domain adaptation.
10.7507/1001-5515.202105033
- Author:
Ziming XU
1
;
Yueying ZHOU
1
;
Xuyun WEN
1
;
Yifan NIU
2
;
Ziyu LI
2
;
Xijia XU
3
;
Daoqiang ZHANG
1
;
Xia WU
2
Author Information
1. College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, P. R. China.
2. College of Artificial Intelligence, Beijing Normal University, Beijing 100875, P. R. China.
3. Department of Psychiatry, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing 211106, P. R. China.
- Publication Type:Journal Article
- Keywords:
Cross subject;
Domain adaptation;
Electroencephalogram signal;
Functional connectivity;
Personality assessment
- MeSH:
Algorithms;
Brain;
Electroencephalography/methods*;
Emotions;
Humans;
Personality Assessment
- From:
Journal of Biomedical Engineering
2022;39(2):257-266
- CountryChina
- Language:Chinese
-
Abstract:
The research shows that personality assessment can be achieved by regression model based on electroencephalogram (EEG). Most of existing researches use event-related potential or power spectral density for personality assessment, which can only represent the brain information of a single region. But some research shows that human cognition is more dependent on the interaction of brain regions. In addition, due to the distribution difference of EEG features among subjects, the trained regression model can not get accurate results of cross subject personality assessment. In order to solve the problem, this research proposes a personality assessment method based on EEG functional connectivity and domain adaption. This research collected EEG data from 45 normal people under different emotional pictures (positive, negative and neutral). Firstly, the coherence of 59 channels in 5 frequency bands was taken as the original feature set. Then the feature-based domain adaptation was used to map the feature to a new feature space. It can reduce the distribution difference between training and test set in the new feature space, so as to reduce the distribution difference between subjects. Finally, the support vector regression model was trained and tested based on the transformed feature set by leave-one-out cross-validation. What's more, this paper compared the methods used in previous researches. The results showed that the method proposed in this paper improved the performance of regression model and obtained better personality assessment results. This research provides a new method for personality assessment.
