In recent years, emotion classification using electroencephalography (EEG) has attracted much attention with the rapid development of machine learning techniques and various applications of brain-computer interfacing. In this study, a general model for emotion recognition was created using a large dataset of 116 participants' EEG responses to happy and fearful videos. We compared discrete and dimensional emotion models, assessed various popular feature extraction methods, evaluated the efficacy of feature selection algorithms, and examined the performance of 2 classification algorithms. An average test accuracy of 76% was obtained using higher-order spectral features with a support vector machine for discrete emotion classification. An accuracy of up 79% was achieved on the subset of classifiable participants. Finally, the stability of EEG patterns in emotion recognition was examined over time by evaluating consistency across sessions. / Thesis / Master of Science (MSc)
| Identifer | oai:union.ndltd.org:mcmaster.ca/oai:macsphere.mcmaster.ca:11375/24287 |
| Date | January 2018 |
| Creators | Jamil, Sara |
| Contributors | Sonnadara, Ranil, Becker, Suzanna, Computational Engineering and Science |
| Source Sets | McMaster University |
| Language | English |
| Detected Language | English |
| Type | Thesis |
Page generated in 0.002 seconds