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Open AccessArticle

Evaluation of Features in Detection of Dislike Responses to Audio–Visual Stimuli from EEG Signals

1
Artificial Intelligence Laboratory, Faculty of Computer Science and Automation, Technical University of Varna, 1 Studentska str., 9010 Varna, Bulgaria
2
School of Engineering and Computer Science, University of Hertfordshire, College Lane Campus, Hatfield AL10 9AB, UK
*
Author to whom correspondence should be addressed.
Computers 2020, 9(2), 33; https://doi.org/10.3390/computers9020033
Received: 1 March 2020 / Revised: 14 April 2020 / Accepted: 17 April 2020 / Published: 20 April 2020
(This article belongs to the Special Issue Machine Learning for EEG Signal Processing)
There is a strong correlation between the like/dislike responses to audio–visual stimuli and the emotional arousal and valence reactions of a person. In the present work, our attention is focused on the automated detection of dislike responses based on EEG activity when music videos are used as audio–visual stimuli. Specifically, we investigate the discriminative capacity of the Logarithmic Energy (LogE), Linear Frequency Cepstral Coefficients (LFCC), Power Spectral Density (PSD) and Discrete Wavelet Transform (DWT)-based EEG features, computed with and without segmentation of the EEG signal, on the dislike detection task. We carried out a comparative evaluation with eighteen modifications of the above-mentioned EEG features that cover different frequency bands and use different energy decomposition methods and spectral resolutions. For that purpose, we made use of Naïve Bayes classifier (NB), Classification and regression trees (CART), k-Nearest Neighbors (kNN) classifier, and support vector machines (SVM) classifier with a radial basis function (RBF) kernel trained with the Sequential Minimal Optimization (SMO) method. The experimental evaluation was performed on the well-known and widely used DEAP dataset. A classification accuracy of up to 98.6% was observed for the best performing combination of pre-processing, EEG features and classifier. These results support that the automated detection of like/dislike reactions based on EEG activity is feasible in a personalized setup. This opens opportunities for the incorporation of such functionality in entertainment, healthcare and security applications. View Full-Text
Keywords: physiological signals; electroencephalography (EEG); emotion recognition; detection of negative emotional states; Linear Frequency Cepstral Coefficients (LFCC); Logarithmic Energy (LogE); Power Spectral Density (PSD); Discrete Wavelet Transform (DWT); Naïve Bayes classification (NB); classification and regression threes (CART); k-Nearest Neighbors classifier (kNN); Support Vector Machine (SVM) physiological signals; electroencephalography (EEG); emotion recognition; detection of negative emotional states; Linear Frequency Cepstral Coefficients (LFCC); Logarithmic Energy (LogE); Power Spectral Density (PSD); Discrete Wavelet Transform (DWT); Naïve Bayes classification (NB); classification and regression threes (CART); k-Nearest Neighbors classifier (kNN); Support Vector Machine (SVM)
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Feradov, F.; Mporas, I.; Ganchev, T. Evaluation of Features in Detection of Dislike Responses to Audio–Visual Stimuli from EEG Signals. Computers 2020, 9, 33.

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