The Classification Algorithm of Users’ Emotion Using Brain-Wave 


Vol. 39,  No. 2, pp. 122-129, Feb.  2014


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  Abstract

In this study, emotion-classification gathered from users was performed, classification-experiments were then conducted using SVM(Support Vector Machine) and K-means algorithm. Total 15 numbers of channels; CP6, Cz, FC2, T7. PO4, AF3, CP1, CP2, C3, F3, FC6, C4, Oz, T8 and F8 among 32 members of the channels measured were adapted in Brain signals which indicated obvious the classification of emotions in previous researches. To extract emotion, watching DVD and IAPS(International Affective Picture System) which is a way to stimulate with photos were applied and SAM(Self-Assessment Manikin) was used in emotion-classification to users’ emotional conditions. The collected users’ Brain-wave signals gathered had been pre-processing using FIR filter and artifacts(eye-blink) were then deleted by ICA(independence component Analysis) using. The data pre-processing were conveyed into frequency analysis for feature extraction through FFT. At last, the experiment was conducted suing classification algorithm; Although, K-means extracted 70% of results, SVM showed better accuracy which extracted 71.85% of results. Then, the results of previous researches adapted SVM were comparatively analyzed.

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  Cite this article

[IEEE Style]

H. Lee, D. Shin, D. Shin, "The Classification Algorithm of Users’ Emotion Using Brain-Wave," The Journal of Korean Institute of Communications and Information Sciences, vol. 39, no. 2, pp. 122-129, 2014. DOI: .

[ACM Style]

Hyun-ju Lee, Dong-il Shin, and Dong-kyoo Shin. 2014. The Classification Algorithm of Users’ Emotion Using Brain-Wave. The Journal of Korean Institute of Communications and Information Sciences, 39, 2, (2014), 122-129. DOI: .

[KICS Style]

Hyun-ju Lee, Dong-il Shin, Dong-kyoo Shin, "The Classification Algorithm of Users’ Emotion Using Brain-Wave," The Journal of Korean Institute of Communications and Information Sciences, vol. 39, no. 2, pp. 122-129, 2. 2014.

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pISSN : 1226 - 4717
eISSN : 2287 - 3880