An Innovative Approach to Classification of Emotions in EEG Signal for the Use in Neuromarketing Research

Paweł Tarnowski, Marcin Kołodziej, Andrzej Majkowski, and Remigiusz Jan Rak


electroencephalography, EEG, emotion recognition, neuromarketing, classification, decision fusion


The aim of the article is to present an innovative algorithm to recognize pleasant, unpleasant and neutral emotions in EEG signal for the use in neuromarketing research. Modification of the traditional method lies in the fact that, after the step of emotion classification for each individual user, a process of decision fusion takes place. The authors tested the developed solution on 12 people aged from 22 to 54. Emotions were evoked by showing selected images on a computer screen. EEG signal was registered by two bipolar channels with the use of only 4 electrodes. The classification of emotions was done on the basis of the results obtained for all the participants throughout a decision fusion. It has been shown that this method works very well for neuromarketing research.

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