Subject-dependent and subject-independent emotional classification of CMAC-based features using EFuNN
Emotions are postulated to be generated at the brain. To capture the brain activities during emotional processing, several neuro-imaging techniques have been adopted, including electroencephalogram (EEG). In the existing studies, different techniques have been employed to extract features from EEG s...
| Main Authors: | , , |
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| Format: | Conference or Workshop Item |
| Language: | English English |
| Published: |
International Society of Computers and Their Applications (ISCA)
2014
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| Subjects: | |
| Online Access: | http://irep.iium.edu.my/43492/ http://irep.iium.edu.my/43492/ http://irep.iium.edu.my/43492/1/43492_Subject-dependen_complete.pdf http://irep.iium.edu.my/43492/2/43492_Subject-dependen_scopus.pdf |
| Summary: | Emotions are postulated to be generated at the brain. To capture the brain activities during emotional processing, several neuro-imaging techniques have been adopted, including electroencephalogram (EEG). In the existing studies, different techniques have been employed to extract features from EEG signals for emotion classification. However, existing feature extraction techniques do not consider spatial and temporal neural-dynamics of emotion. Furthermore, the non-linearity of EEG and self-adaptive of neural activations are disregard. Therefore, the classification accuracy of any feature extraction technique is inconsistent when applied with different classifiers. Hence, in this study, a new feature extraction technique that inculcates the qualities of EEG signal and the behavior neural activations based on Cerebellar Model Articulation Controller (CMAC) model is proposed. Classification performance of calm, fear, happiness and sadness using Evolving Fuzzy Neural Network (EFuNN) classifiers are compared based on subject-dependent and subject-independent validations. It is observed that the proposed technique is able to yield accuracy of above 50% to above 90% for subject-dependent classification. For subject-independent approach, the highest accuracy is barely 40%. The results suggest that this approach is comparable as a feature extraction technique for classifying emotions. |
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