The research of EEG feature extraction and classification for subjects with different organizational commitment

• Published in: MATEC web of conferences Vol. 355; p. 3042
• Main Authors: Zhang, Rui, Wang, Ziyang, Liu, Yu
• Format: Journal Article Conference Proceeding
• Language: English
• Published: Les Ulis EDP Sciences 2022
• Subjects: Brain; Classification; Clustering; eeg feature extraction; Evaluation; Feature extraction; functional brain network; Machine learning; Organizational aspects; organizational commitment; Permutations; Personality; Personality traits; Power spectral density; Questionnaires; Technology assessment
• ISSN: 2261-236X; 2274-7214; 2261-236X
• DOI: 10.1051/matecconf/202235503042

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.