Emotion Recognition using EEG Signals with Relative Power Values and Bayesian Network
- Authors
- Ko, Kwang-Eun; Yang, Hyun-Chang; Sim, Kwee-Bo
- Issue Date
- Oct-2009
- Publisher
- INST CONTROL ROBOTICS & SYSTEMS, KOREAN INST ELECTRICAL ENGINEERS
- Keywords
- Bayesian network; electroencephalogram (EEG); emotion recognition; relative power value
- Citation
- INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS, v.7, no.5, pp 865 - 870
- Pages
- 6
- Journal Title
- INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS
- Volume
- 7
- Number
- 5
- Start Page
- 865
- End Page
- 870
- URI
- https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/22988
- DOI
- 10.1007/s12555-009-0521-0
- ISSN
- 1598-6446
2005-4092
- 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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Collections - College of ICT Engineering > School of Electrical and Electronics Engineering > 1. Journal Articles
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