CMAC-based computational model of affects (CCMA) for profiling emotion from EEG signals
Several studies have been performed to profile emotions using EEG signals through affective computing approach. It includes data acquisition, signal pre-processing, feature extraction and classification. Different combinations of feature extraction and classification techniques have been propos...
| Main Authors: | , , , |
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| Format: | Proceeding Paper |
| Language: | English |
| Published: |
2014
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| Subjects: | |
| Online Access: | http://irep.iium.edu.my/40480/ http://irep.iium.edu.my/40480/1/40480.pdf |
| _version_ | 1848781962985078784 |
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| author | Yaacob, Hamwira Sakti Abdul Rahman, Abdul Wahab Alshaikhli, Imad Fakhri Taha Kamaruddin, Norhaslinda |
| author_facet | Yaacob, Hamwira Sakti Abdul Rahman, Abdul Wahab Alshaikhli, Imad Fakhri Taha Kamaruddin, Norhaslinda |
| author_sort | Yaacob, Hamwira Sakti |
| building | IIUM Repository |
| collection | Online Access |
| description | Several studies have been performed to profile
emotions using EEG signals through affective computing
approach. It includes data acquisition, signal pre-processing,
feature extraction and classification. Different combinations of
feature extraction and classification techniques have been
proposed. However, the results are subjective. Very few studies
include subject-independent classification. In this paper, a new
profiling model, known as CMAC-based Computational Model
of Affects (CCMA), is proposed. ), CMAC is presumed to be a
reasonable model for processing EEG signals with its innate
capabilities to solve non-linear problems through selforganization
feature mapping (SOFM). Features that are
extracted using CCMA are trained using Evolving Fuzzy
Neural Network (EFuNN) as the classifier. For comparison,
classification of emotions using features that are derived from
power spectral density (PSD) was also performed. The results
shows that the performance of using CCMA for profiling
emotions outperforms the performance of classifying emotions
from PSD features. |
| first_indexed | 2025-11-14T15:57:54Z |
| format | Proceeding Paper |
| id | iium-40480 |
| institution | International Islamic University Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T15:57:54Z |
| publishDate | 2014 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | iium-404802020-12-16T17:15:41Z http://irep.iium.edu.my/40480/ CMAC-based computational model of affects (CCMA) for profiling emotion from EEG signals Yaacob, Hamwira Sakti Abdul Rahman, Abdul Wahab Alshaikhli, Imad Fakhri Taha Kamaruddin, Norhaslinda QA75 Electronic computers. Computer science Several studies have been performed to profile emotions using EEG signals through affective computing approach. It includes data acquisition, signal pre-processing, feature extraction and classification. Different combinations of feature extraction and classification techniques have been proposed. However, the results are subjective. Very few studies include subject-independent classification. In this paper, a new profiling model, known as CMAC-based Computational Model of Affects (CCMA), is proposed. ), CMAC is presumed to be a reasonable model for processing EEG signals with its innate capabilities to solve non-linear problems through selforganization feature mapping (SOFM). Features that are extracted using CCMA are trained using Evolving Fuzzy Neural Network (EFuNN) as the classifier. For comparison, classification of emotions using features that are derived from power spectral density (PSD) was also performed. The results shows that the performance of using CCMA for profiling emotions outperforms the performance of classifying emotions from PSD features. 2014-11-17 Proceeding Paper PeerReviewed application/pdf en http://irep.iium.edu.my/40480/1/40480.pdf Yaacob, Hamwira Sakti and Abdul Rahman, Abdul Wahab and Alshaikhli, Imad Fakhri Taha and Kamaruddin, Norhaslinda (2014) CMAC-based computational model of affects (CCMA) for profiling emotion from EEG signals. In: 5th International Conference on Information and Communication Technology for The Muslim World (ICT4M 2014), 17th - 19th November 2014, Kuching, Sarawak, Malaysia. (Unpublished) http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7020584 |
| spellingShingle | QA75 Electronic computers. Computer science Yaacob, Hamwira Sakti Abdul Rahman, Abdul Wahab Alshaikhli, Imad Fakhri Taha Kamaruddin, Norhaslinda CMAC-based computational model of affects (CCMA) for profiling emotion from EEG signals |
| title | CMAC-based computational model of affects (CCMA) for profiling emotion from EEG signals |
| title_full | CMAC-based computational model of affects (CCMA) for profiling emotion from EEG signals |
| title_fullStr | CMAC-based computational model of affects (CCMA) for profiling emotion from EEG signals |
| title_full_unstemmed | CMAC-based computational model of affects (CCMA) for profiling emotion from EEG signals |
| title_short | CMAC-based computational model of affects (CCMA) for profiling emotion from EEG signals |
| title_sort | cmac-based computational model of affects (ccma) for profiling emotion from eeg signals |
| topic | QA75 Electronic computers. Computer science |
| url | http://irep.iium.edu.my/40480/ http://irep.iium.edu.my/40480/ http://irep.iium.edu.my/40480/1/40480.pdf |