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...
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| Main Authors: | , , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2014
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| Subjects: | |
| Online Access: | http://irep.iium.edu.my/40480/1/40480.pdf http://irep.iium.edu.my/40480/ http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7020584 |
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| Summary: | 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. |
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