Emotion classification based on gamma-band EEG

Author

Li, Mu ; Lu, Bao-Liang

Author_Institution

Dept. of Comput. Sci. & Eng., Shanghai Jiao Tong Univ., Shanghai, China

fYear

2009

fDate

3-6 Sept. 2009

Firstpage

1223

Lastpage

1226

Abstract

In this paper, we use EEG signals to classify two emotions-happiness and sadness. These emotions are evoked by showing subjects pictures of smile and cry facial expressions. We propose a frequency band searching method to choose an optimal band into which the recorded EEG signal is filtered. We use common spatial patterns (CSP) and linear-SVM to classify these two emotions. To investigate the time resolution of classification, we explore two kinds of trials with lengths of 3s and 1s. Classification accuracies of 93.5% plusmn 6.7% and 93.0%plusmn6.2% are achieved on 10 subjects for 3s-trials and 1s-trials, respectively. Our experimental results indicate that the gamma band (roughly 30-100 Hz) is suitable for EEG-based emotion classification.

Keywords

electroencephalography; emotion recognition; medical signal processing; support vector machines; common spatial patterns; emotion classification; emotions classification; facial expressions; frequency band searching; gamma band EEG; happiness classification; linear SVM classifier; sadness classification; support vector machine; Adult; Artificial Intelligence; Biomedical Engineering; Electroencephalography; Emotions; Female; Humans; Linear Models; Male; Pattern Recognition, Automated; Photic Stimulation; Signal Processing, Computer-Assisted;

fLanguage

English

Publisher

ieee

Conference_Titel

Engineering in Medicine and Biology Society, 2009. EMBC 2009. Annual International Conference of the IEEE

Conference_Location

Minneapolis, MN

ISSN

1557-170X

Print_ISBN

978-1-4244-3296-7

Electronic_ISBN

1557-170X

Type

conf

DOI

10.1109/IEMBS.2009.5334139

Filename

5334139