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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| Format: | Article |
| Language: | English |
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Springer-Verlag, Berlin, Germany
2013
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| Subjects: | |
| Online Access: | http://irep.iium.edu.my/34356/ http://irep.iium.edu.my/34356/1/Emotion_Recognition_Model_Using_Source-Temporal_Features_and_Fuzzy.pdf |
| _version_ | 1848780897599356928 |
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| author | Shams, Wafa Khazal Abdul Rahman, Abdul Wahab Taha Alshaikhli, Imad Fakhri |
| author_facet | Shams, Wafa Khazal Abdul Rahman, Abdul Wahab Taha Alshaikhli, Imad Fakhri |
| author_sort | Shams, Wafa Khazal |
| building | IIUM Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T15:40:58Z |
| format | Article |
| id | iium-34356 |
| institution | International Islamic University Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T15:40:58Z |
| publishDate | 2013 |
| publisher | Springer-Verlag, Berlin, Germany |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | iium-343562018-05-24T07:43:59Z http://irep.iium.edu.my/34356/ Emotion recognition model using source-temporal features and fuzzy Shams, Wafa Khazal Abdul Rahman, Abdul Wahab Taha Alshaikhli, Imad Fakhri QA75 Electronic computers. Computer science 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. Springer-Verlag, Berlin, Germany 2013 Article PeerReviewed application/pdf en http://irep.iium.edu.my/34356/1/Emotion_Recognition_Model_Using_Source-Temporal_Features_and_Fuzzy.pdf Shams, Wafa Khazal and Abdul Rahman, Abdul Wahab and Taha Alshaikhli, Imad Fakhri (2013) Emotion recognition model using source-temporal features and fuzzy. Lecture Notes in Computer Science (LNCS), 8226 (1). pp. 577-584. ISSN 1611-3349 (O), 0302-9743 (P) http://www.springer.com/computer/lncs?SGWID=0-164-6-1068921-0 |
| spellingShingle | QA75 Electronic computers. Computer science Shams, Wafa Khazal Abdul Rahman, Abdul Wahab Taha Alshaikhli, Imad Fakhri Emotion recognition model using source-temporal features and fuzzy |
| title | Emotion recognition model using source-temporal features and fuzzy |
| title_full | Emotion recognition model using source-temporal features and fuzzy |
| title_fullStr | Emotion recognition model using source-temporal features and fuzzy |
| title_full_unstemmed | Emotion recognition model using source-temporal features and fuzzy |
| title_short | Emotion recognition model using source-temporal features and fuzzy |
| title_sort | emotion recognition model using source-temporal features and fuzzy |
| topic | QA75 Electronic computers. Computer science |
| url | http://irep.iium.edu.my/34356/ http://irep.iium.edu.my/34356/ http://irep.iium.edu.my/34356/1/Emotion_Recognition_Model_Using_Source-Temporal_Features_and_Fuzzy.pdf |