EEG-based emotion recognition using frequency domain features and support vector machines

Authors:
Xiao-Wei Wang;Dan Nie;Bao-Liang Lu
Affiliations:
Center for Brain-Like Computing and Machine Intelligence Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China;Center for Brain-Like Computing and Machine Intelligence Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China;Center for Brain-Like Computing and Machine Intelligence Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China
Venue:
ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part I
Year:
2011

Citing 4
Cited 0

Affective computing

Affective computing
Recognizing Facial Expressions in Image Sequences Using Local Parameterized Models of Image Motion

International Journal of Computer Vision
Toward Machine Emotional Intelligence: Analysis of Affective Physiological State

IEEE Transactions on Pattern Analysis and Machine Intelligence - Graph Algorithms and Computer Vision
Emotion assessment: arousal evaluation using EEG's and peripheral physiological signals

MRCS'06 Proceedings of the 2006 international conference on Multimedia Content Representation, Classification and Security

Quantified Score

Hi-index	0.00

Visualization

Abstract

Information about the emotional state of users has become more and more important in human-machine interaction and brain-computer interface. This paper introduces an emotion recognition system based on electroencephalogram (EEG) signals. Experiments using movie elicitation are designed for acquiring subject's EEG signals to classify four emotion states, joy, relax, sad, and fear. After pre-processing the EEG signals, we investigate various kinds of EEG features to build an emotion recognition system. To evaluate classification performance, k-nearest neighbor (kNN) algorithm, multilayer perceptron and support vector machines are used as classifiers. Further, a minimum redundancy-maximum relevance method is used for extracting common critical features across subjects. Experimental results indicate that an average test accuracy of 66.51% for classifying four emotion states can be obtained by using frequency domain features and support vector machines.