Classification of the emotional states based on the EEG signal processing

The paper proposes a method for the classification of EEG signal based on machine learning methods. We analyzed the data from an EEG experiment consisting of affective picture stimuli presentation, and tested automatic recognition of the individual emotional states from the EEG signal using Bayes cl...

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Bibliographic Details
Published inIEEE journal of biomedical and health informatics pp. 1 - 4
Main Authors Macas, M., Vavrecka, M., Gerla, V., Lhotska, L.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.11.2009
Subjects
Bayes classifier
Biomedical signal processing
Brain
Data analysis
EEG
Electroencephalography
emotional states
feature selection
Frequency
Learning systems
Paper technology
Signal analysis
Signal processing
SOM
Testing
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Summary:The paper proposes a method for the classification of EEG signal based on machine learning methods. We analyzed the data from an EEG experiment consisting of affective picture stimuli presentation, and tested automatic recognition of the individual emotional states from the EEG signal using Bayes classifier. The mean accuracy was about 75 percent, but we were not able to select universal features for classification of all subjects, because of inter-individual differences in the signal. We also identified correlation between the classification error and the extroversion-introversion personality trait measured by EPQ-R test. Introverts have lower excitation threshold so we are able to detect the differences in their EEG activity with better accuracy. Furthermore, the use of Kohonen's self-organizing map for visualization is suggested and demonstrated on one subject.
ISSN:2168-2194
DOI:10.1109/ITAB.2009.5394429