Journal
INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS
Volume 7, Issue 5, Pages 865-870Publisher
INST CONTROL ROBOTICS & SYSTEMS, KOREAN INST ELECTRICAL ENGINEERS
DOI: 10.1007/s12555-009-0521-0
Keywords
Bayesian network; electroencephalogram (EEG); emotion recognition; relative power value
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Funding
- Korean Government [2008-0060738]
- National Research Foundation of Korea [2008-0060738] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
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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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