Earticle

초록

영어

There are several methods of psychophysiological data collection from humans such as, Electrocardiogram (ECG), Galvanic Skin Response (GSR), Electromyography (EMG), and Electroencephalography (EEG). This paper is presenting the emotion recognition of EEG brain signals using Support Vector Machine (SVM). The emotions were elicited in the subjects using emotion related stimuli. We used the emotional stimuli from the International Affective Picture System (IAPS) database in this research. These stimuli belonged to five types of emotions in our experiment such as, happy, calm, neutral, sad and scared. The raw EEG brain signals were preprocessed to remove the artifacts. We introduced a feature extraction method using Hjorth parameters. The set of features were extracted from preprocessed EEG signals of each subject, separately. The combined feature set of all subjects was processed through SVM. The results had shown the 70 % accuracy of emotion recognition in arousal-valence domain over 30 subjects.

목차

Abstract
 1. Introduction
 2. Materials and Methods
 3. Results and Discussion
 4. Conclusion
 References

키워드

저자

• Raja Majid Mehmood [ Division of Computer Science and Engineering ]
• Hyo Jong Lee [ Division of Computer Science and Engineering Center for Advanced Image and Information Technology ] Corresponding author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Bio-Science and Bio-Technology

• 간기

  격월간

• pISSN

  2233-7849

• 수록기간

  2009~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605