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EEG-Analysis for the Detection of True Emotion or Pretension

EEG-Analysis for the Detection of True Emotion or Pretension

Reshma Kar, Amit Konar, Aruna Chakraborty
Copyright: © 2015 |Pages: 16
ISBN13: 9781466672789|ISBN10: 1466672781|EISBN13: 9781466672796
DOI: 10.4018/978-1-4666-7278-9.ch013
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MLA

Kar, Reshma, et al. "EEG-Analysis for the Detection of True Emotion or Pretension." Handbook of Research on Synthesizing Human Emotion in Intelligent Systems and Robotics, edited by Jordi Vallverdú, IGI Global, 2015, pp. 283-298. https://doi.org/10.4018/978-1-4666-7278-9.ch013

APA

Kar, R., Konar, A., & Chakraborty, A. (2015). EEG-Analysis for the Detection of True Emotion or Pretension. In J. Vallverdú (Ed.), Handbook of Research on Synthesizing Human Emotion in Intelligent Systems and Robotics (pp. 283-298). IGI Global. https://doi.org/10.4018/978-1-4666-7278-9.ch013

Chicago

Kar, Reshma, Amit Konar, and Aruna Chakraborty. "EEG-Analysis for the Detection of True Emotion or Pretension." In Handbook of Research on Synthesizing Human Emotion in Intelligent Systems and Robotics, edited by Jordi Vallverdú, 283-298. Hershey, PA: IGI Global, 2015. https://doi.org/10.4018/978-1-4666-7278-9.ch013

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Abstract

Several lobes in the human brain are involved differently in the arousal, processing and manifestation of emotion in facial expression, vocal intonation and gestural patterns. Sometimes people suppress their bodily manifestations to pretend their emotions. Detection of emotion and pretension is an open problem in emotion research. The chapter presents an analysis of EEG signals to detect true emotion/pretension: first by extracting the neural connectivity among selected brain lobes during arousal and manifestation of a true emotion, and then by testing whether the connectivity among the lobes are maintained while encountering an emotional context. In case the connectivity is manifested, the arousal of emotion is regarded as true emotion, otherwise it is considered as a pretension. Experimental results confirm that for positive emotions, the decoding accuracy of true (false) emotions is as high as 88% (72%), while for negative emotions, the classification accuracy falls off by a 12% margin for true emotions and 8% margin for false emotions. The proposed method has wide-spread applications to detect criminals, frauds and anti-socials.

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