MATEC Web Conf.
Volume 355, 20222021 International Conference on Physics, Computing and Mathematical (ICPCM2021)
|Number of page(s)||10|
|Section||Computing Methods and Computer Application|
|Published online||12 January 2022|
The research of EEG feature extraction and classification for subjects with different organizational commitment
1 School of Artificial Intelligence, University of Chinese Academy of Sciences, China
2 Institute of Automation, Chinese Academy of Sciences, China
* Corresponding author: firstname.lastname@example.org
With the development of EEG analysis technology, researchers have gradually explored the correlation between personality trait (such as Big Five personality) and EEG. However, there are still many challenges in model construction. In this paper, we tried to classify the people with different organizational commitment personality trait through EEG. Firstly, we organized the participants to complete the organizational commitment questionnaire and recorded their resting state EEG. We divided 10 subjects into two classes (positive and negative) according to the questionnaire scores. Then, various EEG features including power spectral density, microstate, functional brain network and nonlinear features from segmented EEG sample were extracted as the input of different machine learning classifiers. Next, several evaluation metrics were used to evaluate the results of the cross-validation experiment. Finally, the results show that the EEG power in α band, the weighted clustering coefficient of functional brain network and the Permutation Entropy of EEG are relatively good features for this classification task. Furthermore, the highest classification accuracy rate can reach 79.9% with 0.87 AUC (the area under the ROC). The attempts in this paper may serve as the basis for our future research.
Key words: EEG feature extraction / Machine learning / Power spectral density / Functional brain network / Organizational commitment
© The Authors, published by EDP Sciences, 2022
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