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

Firgan Feradov, Iosif Mporas, Todor Ganchev

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)
19 Downloads (Pure)


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.
Original languageEnglish
Article number33
Number of pages11
Issue number2
Publication statusPublished - 20 Apr 2020


  • Classification and regression threes (CART)
  • Detection of negative emotional states
  • Discrete Wavelet Transform (DWT)
  • Electroencephalography (EEG)
  • Emotion recognition
  • K-Nearest Neighbors classifier (kNN)
  • Linear Frequency Cepstral Coefficients (LFCC)
  • Logarithmic Energy (LogE)
  • Naïve Bayes classification (NB)
  • Physiological signals
  • Power Spectral Density (PSD)
  • Support Vector Machine (SVM)


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