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Title

Emotional State Recognition Based on Brain and Peripheral Signals Using Multi-Class Optimized FSVM Classifiers

پنجمین کنفرانس ملی مهندسی برق و مکاترونیک ایران
Year: 1398
COI: ICELE05_205
Language: EnglishView: 363
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Authors

Kamran Mohammad Sharifi - Department of Electrical Engineering Amirkabir University of Technology Tehran, Iran
Ali Raziabadi - Department of Electrical Engineering Amirkabir University of Technology Tehran, Iran
Behzad Farzanegan - Department of Electrical Engineering Amirkabir University of Technology Tehran, Iran
Mohammad Hossein zadeh - Department of Electrical Engineering Amirkabir University of Technology Tehran, Iran
Mohammad Bagher Menhaj - Department of Electrical Engineering Amirkabir University of Technology Tehran, Iran

Abstract:

Emotions are excellent sources of information in communication, and emotion classification is one of the most sophisticated topics in biomedical signal research. This paper proposes a novel approach based on a fuzzy support vector machine (FSVM) classifier to recognize two of the human’s emotion states (Arousal and Valence), each of which includes positive and negative states, according to Electroencephalography (EEG) signals. The purpose is to attain high accuracy via fuzzification of training data and test data. Data fuzzification not only reduces noise level and data size, but also increases accuracy. In this paper, the KPCA method is used in order to reduce the space dimension due to the greatness of feature space as well as the time consuming nature of SVM training process. In order to obtain the optimal parameters of FSVM core and KPCA parameters - for maximization of the accuracy - for a two-dimensional plan (Valence-Arousal), the Tabu Search Algorithm (TSA) is used. The simulation results demonstrate that the optimized fuzzy classifier improved diagnosis of emotional states by about 3 to 7 percent.

Keywords:

emotion classification , optimization , fuzzy support vector machine (FSVM) , KPCA , Tabu search algorithm (TSA)

Paper COI Code

This Paper COI Code is ICELE05_205. Also You can use the following address to link to this article. This link is permanent and is used as an article registration confirmation in the Civilica reference:

https://civilica.com/doc/988536/

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Mohammad Sharifi, Kamran and Raziabadi, Ali and Farzanegan, Behzad and Hossein zadeh, Mohammad and Menhaj, Mohammad Bagher,1398,Emotional State Recognition Based on Brain and Peripheral Signals Using Multi-Class Optimized FSVM Classifiers,Fifth National Conference on Electrical and Mechatronics Engineering of Iran,Tehran,https://civilica.com/doc/988536

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Type of center: دانشگاه دولتی
Paper count: 22,259
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