EEG-based Emotion Classification using Innovative Features and Combined SVM and HMM Classifier

Publisher:
IEEE
Publication Type:
Conference Proceeding
Citation:
Proceedings of the 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC’17), 2017, pp. 489 - 492
Issue Date:
2017-07-11
Full metadata record
Files in This Item:
Filename Description Size
609277.pdfAccepted Manuscript457 kB
Adobe PDF
Emotion classification is one of the state-of-theart topics in biomedical signal research, and yet a significant portion remains unknown. This paper offers a novel approach with a combined classifier to recognise human emotion states based on electroencephalogram (EEG) signal. The objective is to achieve high accuracy using the combined classifier designed, which categorises the extracted features calculated from time domain features and Discrete Wavelet Transform (DWT). Two innovative designs are involved in this project: a novel variable is established as a new feature and a combined SVM and HMM classifier is developed. The result shows that the joined features raise the accuracy by 5% on valence axis and 1.5% on arousal axis. The combined classifier can improve the accuracy by 3% comparing with SVM classifier. One of the important applications for high accuracy emotion classification system is offering a powerful tool for psychologists to diagnose emotion related mental diseases and the system developed in this project has the potential to serve such purpose.
Please use this identifier to cite or link to this item: