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Emotion recognition using EEG signals with relative power values and Bayesian network

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Abstract

Many researchers use electroencephalograms (EEGs) to study brain activity in the context of seizures, epilepsy, and lie detection. It is desirable to eliminate EEG artifacts to improve signal collection. In this paper, we propose an emotion recognition system for human brain signals using EEG signals. We measure EEG signals relating to emotion, divide them into five frequency ranges on the basis of power spectrum density, and eliminate low frequencies from 0 to 4 Hz to eliminate EEG artifacts. The resulting calculations of the frequency ranges are based on the percentage of the selected range relative to the total range. The calculated values are then compared to standard values from a Bayesian network, calculated from databases. Finally, we show the emotion results as a human face avatar.

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Authors and Affiliations

  1. School of Electrical and Electronics Engineering, Chung-Ang University, 221, Heukseok-dong, Dongjak-gu, Seoul, 156-756, Korea

    Kwang-Eun Ko, Hyun-Chang Yang & Kwee-Bo Sim

Authors
  1. Kwang-Eun Ko
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  2. Hyun-Chang Yang
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  3. Kwee-Bo Sim
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Corresponding author

Correspondence to Kwee-Bo Sim.

Additional information

Recommended by Editor Young-Hoon Joo. This work was supported by the Korea Research Foundation Grant funded by the Korean Government (2008-0060738).

Kwang-Eun Ko received the B.S. and M.S. degrees from the Department of Electrical and Electronics Engineering, Chung-Ang University, Seoul, Korea, in 2007 and 2009 respectively. He is currently a candidate for the Ph.D. degree in the School of Electrical and Electronics Engineering at Chung-Ang University. His research interests include machine learning, context awareness, and multimodal emotion recognition.

Hyun-Chang Yang received his M.S. degree from the Department of Industrial Engineering, Soong-sil University, Korea in 2002. He is currently a candidate for the Ph.D. degree in the School of Electrical and Electronics Engineering at Chung-Ang University. His research interests include intelligent robots, home networks, the smart home, ubiquitous sensor networks, and soft computing.

Kwee-Bo Sim received the B.S. and M.S. degrees from the Department of Electronics Engineering at Chung-Ang University, Korea, in 1984 and 1986, respectively, and the Ph.D. degree from the Department of Electronics Engineering at the University of Tokyo, Japan, in 1990. Since 1991, he has been a Faculty Member in the School of Electrical and Electronics Engineering at Chung-Ang University, where he is currently a Professor. His research fields are artificial life, emotion recognition, ubiquitous robots, intelligent systems, computational intelligence, intelligent home and home networks, ubiquitous computing and sense networks, adaptation and machine learning algorithms, neural networks, fuzzy systems, evolutionary computation, multi-agent distributed autonomous robotic systems, artificial immune systems, evolvable hardware, and embedded systems. He is a member of the IEEE, SICE, RSJ, IEEK, KIEE, KIIS, KROS, IEMEK, and is an ICROS Fellow.

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Ko, KE., Yang, HC. & Sim, KB. Emotion recognition using EEG signals with relative power values and Bayesian network. Int. J. Control Autom. Syst. 7, 865–870 (2009). https://doi.org/10.1007/s12555-009-0521-0

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  • DOI: https://doi.org/10.1007/s12555-009-0521-0

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