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http://dx.doi.org/10.9708/jksci.2010.15.10.105

A Study on Algorithm of Emotion Analysis using EEG and HRV  

Chon, Ki-Hwan (한림성심대학 의료기기정보과)
Oh, Ju-Young (경인여자대학 정보미디어학부)
Park, Sun-Hee (강원대학교 컴퓨터과학과)
Jeong, Yeon-Man (강릉원주대학교 정보통신공학과)
Yang, Dong-Il (한림성심대학 인터넷비즈니스과)
Publication Information
Journal of the Korea Society of Computer and Information / v.15, no.10, 2010 , pp. 105-112 More about this Journal
Abstract
In this paper, the bio-signals, such as EEG, ECG were measured with a sensor and their characters were drawn out and analyzed. With results from the analysis, four emotion of rest, concentration, tension and depression were inferred. In order to assess one's emotion, the characteristic vectors were drawn out by applying various ways, including the frequency analysis of the bio-signals like the measured EEG and HRV. RBFN, a neural network of the complex structure of unsupervised and supervised learning, was applied to classify and infer the deducted information. Through experiments, the system suggested in this thesis showed better capability to classify and infer than other systems using a different neural network. As follow-up research tasks, the recognizance rate of the measured bio-signals should be improved. Also, the technology which can be applied to the wired or wireless sensor measuring the bio-signals more easily and to wearable computing should be developed.
Keywords
EEG; bio-signals; HRV; wireless sensor;
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