Comparing the effectiveness of audio and image stimuli on personality assessment task using EEG | |
| Author | Sahoo, Suprava |
| Call Number | AIT RSPR no.CS-22-05 |
| Subject(s) | Electroencephalography Personality assessment |
| Note | A research study submitted in partial fulfillment of the requirements for the degree of Master of Science in Computer Science |
| Publisher | Asian Institute of Technology |
| Abstract | Predicting personality using questionnaires is prone to manipulation. One promising alter- native method is to exploit the induced brain activity from the responses to various stimuli, especially of images. However, the use of audio stimuli is underexplored but can be potentially useful when image is not possible, e.g., visually impaired person. The objective of this research is to automatically detect HEXACO personality traits in individuals by combining electroencephalogram (EEG) and machine learning techniques. 30 healthy participants were presented with emotional audio and image stimuli, then their responses were recorded using an EEG device. For classification, Random Forest, Support Vector Machine (SVM), Logistic Regression, Gradient Boosting Classifier, and K Nearest Neighbor (KNN) are finally being used. Our study also shows that audio stimuli are as good as image stimuli or better performance in some traits of HEXACO. |
| Year | 2022 |
| Type | Research Study Project Report (RSPR) |
| School | School of Engineering and Technology |
| Department | Department of Information and Communications Technologies (DICT) |
| Academic Program/FoS | Computer Science (CS) |
| Chairperson(s) | Chaklam Silpasuwanchai |
| Examination Committee(s) | Chutiporn Anutariya;Dailey, Mathew N. |
| Scholarship Donor(s) | AIT Fellowship |
| Degree | Research Studies Project Report (M. Sc.) - Asian Institute of Technology, 2022 |