Original scientific paper

https://doi.org/10.31803/tg-20220330064309

EEG Based Emotion Prediction with Neural Network Models

F. Kebire Bardak orcid.org/0000-0002-9380-2330 ; Department of Electrical and Electronics Engineering, Bandırma Onyedi Eylül University, Balikesir, Bandirma, Turkey
M. Nuri Seyman ; Department of Electrical and Electronics Engineering, Bandırma Onyedi Eylül University, Balikesir, Bandirma, Turkey
Feyzullah Temurtaş ; (1) Department of Electrical and Electronics Engineering, Bandırma Onyedi Eylül University, Balikesir, Bandirma, Turkey / (2) AINTELIA Artificial Intelligence Technologies Company, 16240, Bursa, Turkey

Abstract

The term "emotion" refers to an individual's response to an event, person, or condition. In recent years, there has been an increase in the number of papers that have studied emotion estimation. In this study, a dataset based on three different emotions, utilized to classify feelings using EEG brainwaves, has been analysed. In the dataset, six film clips have been used to elicit positive and negative emotions from a male and a female. However, there has not been a trigger to elicit a neutral mood. Various classification approaches have been used to classify the dataset, including MLP, SVM, PNN, KNN, and decision tree methods. The Bagged Tree technique which is utilized for the first time has been achieved a 98.60 percent success rate in this study, according to the researchers. In addition, the dataset has been classified using the PNN approach, and achieved a success rate of 94.32 percent.

Keywords

AdaBoost; bagged tree; EEG signals; emotion prediction; multi-layer perceptron; probabilistic neural networks

Hrčak ID:

283785

URI

https://hrcak.srce.hr/283785

Publication date:

23.9.2022.

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