Emotion recognition model using source-temporal features and fuzzy
This paper aims to evaluate the performance of Electroencephalographic (EEG) emotion recognition system (EEG-ER) using source-temporal domain with Takagi- Sugeno-Kang (TSK) fuzzy model. Ten healthy subjects aged 5-6 years were participated in this study. Emotion elicitation procedure has done usi...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Springer-Verlag, Berlin, Germany
2013
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| Subjects: | |
| Online Access: | http://irep.iium.edu.my/34356/1/Emotion_Recognition_Model_Using_Source-Temporal_Features_and_Fuzzy.pdf http://irep.iium.edu.my/34356/ http://www.springer.com/computer/lncs?SGWID=0-164-6-1068921-0 |
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| Summary: | This paper aims to evaluate the performance of Electroencephalographic
(EEG) emotion recognition system (EEG-ER) using source-temporal domain with
Takagi- Sugeno-Kang (TSK) fuzzy model. Ten healthy subjects aged 5-6 years
were participated in this study. Emotion elicitation procedure has done using the
Radbound faces database (RafD). The selected emotions were happy, sad, and
neutral and fear. The results were compared with wavelet coefficients (WC) as
feature extraction method and Regularized Least Square (RLS) and Multi-Layer
Perception (MLP) neural network classifiers from our previous work. Another
comparison was done between affective model of Russell and RafD. The results
show the efficiency of using source-temporal features in emotion recognition
system hence there was a slight difference in accuracy among different classifier;
MLP, RLS and TSK however MLP and TSK results were with high accurate and
stable. Moreover Russell model which is based on positive-negative dimensions
shows high accuracy than RafD model that has positive dimensions. The accuracy
was around 97% using Russell model. |
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