Evaluation of Features in Detection of Dislike Responses to Audio–Visual Stimuli from EEG Signals
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 audi...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2020-04-01
|
| Series: | Computers |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-431X/9/2/33 |
| _version_ | 1797570185856548864 |
|---|---|
| author | Firgan Feradov Iosif Mporas Todor Ganchev |
| author_facet | Firgan Feradov Iosif Mporas Todor Ganchev |
| author_sort | Firgan Feradov |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T20:21:21Z |
| format | Article |
| id | doaj.art-7915dfb87c1544569509787fe9854f83 |
| institution | Directory Open Access Journal |
| issn | 2073-431X |
| language | English |
| last_indexed | 2024-03-10T20:21:21Z |
| publishDate | 2020-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Computers |
| spelling | doaj.art-7915dfb87c1544569509787fe9854f832023-11-19T22:08:03ZengMDPI AGComputers2073-431X2020-04-01923310.3390/computers9020033Evaluation of Features in Detection of Dislike Responses to Audio–Visual Stimuli from EEG SignalsFirgan Feradov0Iosif Mporas1Todor Ganchev2Artificial Intelligence Laboratory, Faculty of Computer Science and Automation, Technical University of Varna, 1 Studentska str., 9010 Varna, BulgariaSchool of Engineering and Computer Science, University of Hertfordshire, College Lane Campus, Hatfield AL10 9AB, UKArtificial Intelligence Laboratory, Faculty of Computer Science and Automation, Technical University of Varna, 1 Studentska str., 9010 Varna, BulgariaThere 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.https://www.mdpi.com/2073-431X/9/2/33physiological signalselectroencephalography (EEG)emotion recognitiondetection of negative emotional statesLinear Frequency Cepstral Coefficients (LFCC)Logarithmic Energy (LogE) |
| spellingShingle | Firgan Feradov Iosif Mporas Todor Ganchev Evaluation of Features in Detection of Dislike Responses to Audio–Visual Stimuli from EEG Signals Computers physiological signals electroencephalography (EEG) emotion recognition detection of negative emotional states Linear Frequency Cepstral Coefficients (LFCC) Logarithmic Energy (LogE) |
| title | Evaluation of Features in Detection of Dislike Responses to Audio–Visual Stimuli from EEG Signals |
| title_full | Evaluation of Features in Detection of Dislike Responses to Audio–Visual Stimuli from EEG Signals |
| title_fullStr | Evaluation of Features in Detection of Dislike Responses to Audio–Visual Stimuli from EEG Signals |
| title_full_unstemmed | Evaluation of Features in Detection of Dislike Responses to Audio–Visual Stimuli from EEG Signals |
| title_short | Evaluation of Features in Detection of Dislike Responses to Audio–Visual Stimuli from EEG Signals |
| title_sort | evaluation of features in detection of dislike responses to audio visual stimuli from eeg signals |
| topic | physiological signals electroencephalography (EEG) emotion recognition detection of negative emotional states Linear Frequency Cepstral Coefficients (LFCC) Logarithmic Energy (LogE) |
| url | https://www.mdpi.com/2073-431X/9/2/33 |
| work_keys_str_mv | AT firganferadov evaluationoffeaturesindetectionofdislikeresponsestoaudiovisualstimulifromeegsignals AT iosifmporas evaluationoffeaturesindetectionofdislikeresponsestoaudiovisualstimulifromeegsignals AT todorganchev evaluationoffeaturesindetectionofdislikeresponsestoaudiovisualstimulifromeegsignals |