Journal
IEEE ACCESS
Volume 8, Issue -, Pages 11907-11916Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2020.2966144
Keywords
Emotion recognition; EEG source reconstruction; inverse solution; difference analysis of active source; time- and frequency-domain features
Categories
Funding
- National Natural Science Foundation of China [61371193]
- Natural Science Foundation for Young Scientists of Shanxi Province, China [201701D221117]
- Taiyuan University of Technology Foundation, China [2016QN24]
- Key Research and Development Project of Shanxi Province, China [201803D31045]
- Scientific and Technological Innovation Project in Higher Education Institutions of Shanxi Province, China [2019L0189]
Ask authors/readers for more resources
Emotion plays a significant role in perceiving external events or situations in daily life. Due to ease of use and relative accuracy, Electroencephalography (EEG)-based emotion recognition has become a hot topic in the affective computing field. However, scalp EEG is a mixed-signal and cannot directly indicate the exact information about active cortex sources of different emotions. In this paper, we analyze the significant differences of active source regions and frequency bands for pairs of emotions-based reconstructed EEG sources using sLORETA, and 26 Brodmann areas are selected as the regions of interest (ROI). And then, six kinds of time- and frequency-domain features from significant active regions and frequency bands are extracted to classify different emotions using support vector machines. Furthermore, we compare the classification performances of emotion features extracted from active source regions and EEG sensors. We have demonstrated that the features from selected source regions can improve the classification accuracy by extensive experiments on the DEAP and TYUT 2.0 EEG-based datasets.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available