ITM Web Conf.
Volume 11, 20172017 International Conference on Information Science and Technology (IST 2017)
|Number of page(s)||10|
|Section||Session VII: Control and Automation|
|Published online||23 May 2017|
Channel Division Based Multiple Classifiers Fusion for Emotion Recognition Using EEG signals
1 Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
2 Beijing Advanced Innovation Center for Future Internet Technology, Beijing University of Technology, Beijing 100024, China
3 International WIC Institute, Beijing University of Technology, Beijing 100024, China
a Corresponding author: firstname.lastname@example.org
With the rapid development of computer technology, pervasive computing and wearable devices, EEG-based emotion recognition has gradually attracted much attention in affecting computing (AC) domain. In this paper, we propose an approach of emotion recognition using EEG signals based on the weighted fusion of multiple base classifiers. These base classifiers based on SVM are constructed using a channel division mechanism according to the neuropsychological theory that different brain areas are differ in processing intensity of emotional information. The outputs of channel base classifiers are integrated by a weighted fusion strategy which is based on the confidence estimation on each emotional label by each base classifier. The evaluation on the DEAP dataset shows that our proposed multiple classifiers fusion method outperforms individual channel base classifiers and the feature fusion method for EEG-based emotion recognition.
© Owned by the authors, published by EDP Sciences, 2017
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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