Classification of the Emotional States Based on the EEG Signal Processing

Kód výsledku v IS VaVaI

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Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Classification of the Emotional States Based on the EEG Signal Processing

Popis výsledku v původním jazyce

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 interindividual differences in the signal. We also identifiedcorrelation 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.

Název v anglickém jazyce

Classification of the Emotional States Based on the EEG Signal Processing

Popis výsledku anglicky

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 interindividual differences in the signal. We also identifiedcorrelation 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.

Druh

D - Stať ve sborníku

CEP obor

JC - Počítačový hardware a software

OECD FORD obor

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Projekt

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Návaznosti

Z - Vyzkumny zamer (s odkazem do CEZ)

Rok uplatnění

2009

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Proceedings of 9th Intrenational Conference on Information Technology and Applications in Biomedicine

ISBN

978-1-4244-5378-8

ISSN

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e-ISSN

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Počet stran výsledku

4

Strana od-do

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Název nakladatele

IEEE

Místo vydání

Piscataway

Místo konání akce

Larnaca

Datum konání akce

5. 11. 2009

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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