Machine Learning Models for Classification of Human Emotions Using Multivariate Brain Signals
Humans can portray different expressions contrary to their emotional state of mind. Therefore, it is difficult to judge humans’ real emotional state simply by judging their physical appearance. Although researchers are working on facial expressions analysis, voice recognition, and gesture recognitio...
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MDPI AG
2022-10-01
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| Series: | Computers |
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| Online Access: | https://www.mdpi.com/2073-431X/11/10/152 |
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| author | Shashi Kumar G. S. Ahalya Arun Niranjana Sampathila R. Vinoth |
| author_facet | Shashi Kumar G. S. Ahalya Arun Niranjana Sampathila R. Vinoth |
| author_sort | Shashi Kumar G. S. |
| collection | DOAJ |
| description | Humans can portray different expressions contrary to their emotional state of mind. Therefore, it is difficult to judge humans’ real emotional state simply by judging their physical appearance. Although researchers are working on facial expressions analysis, voice recognition, and gesture recognition; the accuracy levels of such analysis are much less and the results are not reliable. Hence, it becomes vital to have realistic emotion detector. Electroencephalogram (EEG) signals remain neutral to the external appearance and behavior of the human and help in ensuring accurate analysis of the state of mind. The EEG signals from various electrodes in different scalp regions are studied for performance. Hence, EEG has gained attention over time to obtain accurate results for the classification of emotional states in human beings for human–machine interaction as well as to design a program where an individual could perform a self-analysis of his emotional state. In the proposed scheme, we extract power spectral densities of multivariate EEG signals from different sections of the brain. From the extracted power spectral density (PSD), the features which provide a better feature for classification are selected and classified using long short-term memory (LSTM) and bi-directional long short-term memory (Bi-LSTM). The 2-D emotion model considered for the classification of frontal, parietal, temporal, and occipital is studied. The region-based classification is performed by considering positive and negative emotions. The performance accuracy of our previous model’s results of artificial neural network (ANN), support vector machine (SVM), K-nearest neighbor (K-NN), and LSTM was compared and 94.95% accuracy was received using Bi-LSTM considering four prefrontal electrodes. |
| first_indexed | 2024-03-09T20:26:28Z |
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| institution | Directory Open Access Journal |
| issn | 2073-431X |
| language | English |
| last_indexed | 2024-03-09T20:26:28Z |
| publishDate | 2022-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Computers |
| spelling | doaj.art-b0578c9ca2874649bd0657935e2988892023-11-23T23:36:05ZengMDPI AGComputers2073-431X2022-10-01111015210.3390/computers11100152Machine Learning Models for Classification of Human Emotions Using Multivariate Brain SignalsShashi Kumar G. S.0Ahalya Arun1Niranjana Sampathila2R. Vinoth3Department of Electronics and Communication Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education (MAHE), Manipal 576 104, Karnataka, IndiaDepartment of Electronics and Communication Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education (MAHE), Manipal 576 104, Karnataka, IndiaDepartment of Biomedical Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education (MAHE), Manipal 576 104, Karnataka, IndiaDepartment of Electronics and Communication Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education (MAHE), Manipal 576 104, Karnataka, IndiaHumans can portray different expressions contrary to their emotional state of mind. Therefore, it is difficult to judge humans’ real emotional state simply by judging their physical appearance. Although researchers are working on facial expressions analysis, voice recognition, and gesture recognition; the accuracy levels of such analysis are much less and the results are not reliable. Hence, it becomes vital to have realistic emotion detector. Electroencephalogram (EEG) signals remain neutral to the external appearance and behavior of the human and help in ensuring accurate analysis of the state of mind. The EEG signals from various electrodes in different scalp regions are studied for performance. Hence, EEG has gained attention over time to obtain accurate results for the classification of emotional states in human beings for human–machine interaction as well as to design a program where an individual could perform a self-analysis of his emotional state. In the proposed scheme, we extract power spectral densities of multivariate EEG signals from different sections of the brain. From the extracted power spectral density (PSD), the features which provide a better feature for classification are selected and classified using long short-term memory (LSTM) and bi-directional long short-term memory (Bi-LSTM). The 2-D emotion model considered for the classification of frontal, parietal, temporal, and occipital is studied. The region-based classification is performed by considering positive and negative emotions. The performance accuracy of our previous model’s results of artificial neural network (ANN), support vector machine (SVM), K-nearest neighbor (K-NN), and LSTM was compared and 94.95% accuracy was received using Bi-LSTM considering four prefrontal electrodes.https://www.mdpi.com/2073-431X/11/10/152bidirectional-long short-term memoryelectroencephalogramemotion recognitionlong short-term memorypower spectral density |
| spellingShingle | Shashi Kumar G. S. Ahalya Arun Niranjana Sampathila R. Vinoth Machine Learning Models for Classification of Human Emotions Using Multivariate Brain Signals Computers bidirectional-long short-term memory electroencephalogram emotion recognition long short-term memory power spectral density |
| title | Machine Learning Models for Classification of Human Emotions Using Multivariate Brain Signals |
| title_full | Machine Learning Models for Classification of Human Emotions Using Multivariate Brain Signals |
| title_fullStr | Machine Learning Models for Classification of Human Emotions Using Multivariate Brain Signals |
| title_full_unstemmed | Machine Learning Models for Classification of Human Emotions Using Multivariate Brain Signals |
| title_short | Machine Learning Models for Classification of Human Emotions Using Multivariate Brain Signals |
| title_sort | machine learning models for classification of human emotions using multivariate brain signals |
| topic | bidirectional-long short-term memory electroencephalogram emotion recognition long short-term memory power spectral density |
| url | https://www.mdpi.com/2073-431X/11/10/152 |
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