Factor Analysis for Finding Invariant Neural Descriptors of Human Emotions
A major challenge in decoding human emotions from electroencephalogram (EEG) data is finding representations that are invariant to inter- and intrasubject differences. Most of the previous studies are focused in building an individual discrimination model for every subject (subject dependent model)....
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2018-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2018/6740846 |
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| _version_ | 1849407501546553344 |
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| author | Vitor Pereira Filipe Tavares Petya Mihaylova Valeri Mladenov Petia Georgieva |
| author_facet | Vitor Pereira Filipe Tavares Petya Mihaylova Valeri Mladenov Petia Georgieva |
| author_sort | Vitor Pereira |
| collection | DOAJ |
| description | A major challenge in decoding human emotions from electroencephalogram (EEG) data is finding representations that are invariant to inter- and intrasubject differences. Most of the previous studies are focused in building an individual discrimination model for every subject (subject dependent model). Building subject-independent models is a harder problem due to the high data variability between different subjects and different experiments with the same subject. This paper explores, for the first time, the Factor Analysis as an efficient technique to extract temporal and spatial EEG features suitable to build brain-computer interface for decoding human emotions across various subjects. Our findings show that early waves (temporal window of 200–400 ms after the stimulus onset) carry more information about the valence of the emotion. Also, spatial location of features, with a stronger impact on the emotional valence, occurs in the parietal and occipital regions of the brain. All discrimination models (NN, SVM, kNN, and RF) demonstrate better discrimination rate of the positive valence. These results match closely experimental psychology hypothesis that, during early periods after the stimulus presentation, the brain response—to images with highly positive valence—is stronger. |
| format | Article |
| id | doaj-art-43aeff8cbe0d4a88a87fdc49a4864e45 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2018-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-43aeff8cbe0d4a88a87fdc49a4864e452025-08-20T03:36:02ZengWileyComplexity1076-27871099-05262018-01-01201810.1155/2018/67408466740846Factor Analysis for Finding Invariant Neural Descriptors of Human EmotionsVitor Pereira0Filipe Tavares1Petya Mihaylova2Valeri Mladenov3Petia Georgieva4Department of Electronics Telecommunications and Informatics/IEETA, University of Aveiro, Aveiro, PortugalDepartment of Electronics Telecommunications and Informatics/IEETA, University of Aveiro, Aveiro, PortugalTechnical University of Sofia, Sofia, BulgariaTechnical University of Sofia, Sofia, BulgariaDepartment of Electronics Telecommunications and Informatics/IEETA, University of Aveiro, Aveiro, PortugalA major challenge in decoding human emotions from electroencephalogram (EEG) data is finding representations that are invariant to inter- and intrasubject differences. Most of the previous studies are focused in building an individual discrimination model for every subject (subject dependent model). Building subject-independent models is a harder problem due to the high data variability between different subjects and different experiments with the same subject. This paper explores, for the first time, the Factor Analysis as an efficient technique to extract temporal and spatial EEG features suitable to build brain-computer interface for decoding human emotions across various subjects. Our findings show that early waves (temporal window of 200–400 ms after the stimulus onset) carry more information about the valence of the emotion. Also, spatial location of features, with a stronger impact on the emotional valence, occurs in the parietal and occipital regions of the brain. All discrimination models (NN, SVM, kNN, and RF) demonstrate better discrimination rate of the positive valence. These results match closely experimental psychology hypothesis that, during early periods after the stimulus presentation, the brain response—to images with highly positive valence—is stronger.http://dx.doi.org/10.1155/2018/6740846 |
| spellingShingle | Vitor Pereira Filipe Tavares Petya Mihaylova Valeri Mladenov Petia Georgieva Factor Analysis for Finding Invariant Neural Descriptors of Human Emotions Complexity |
| title | Factor Analysis for Finding Invariant Neural Descriptors of Human Emotions |
| title_full | Factor Analysis for Finding Invariant Neural Descriptors of Human Emotions |
| title_fullStr | Factor Analysis for Finding Invariant Neural Descriptors of Human Emotions |
| title_full_unstemmed | Factor Analysis for Finding Invariant Neural Descriptors of Human Emotions |
| title_short | Factor Analysis for Finding Invariant Neural Descriptors of Human Emotions |
| title_sort | factor analysis for finding invariant neural descriptors of human emotions |
| url | http://dx.doi.org/10.1155/2018/6740846 |
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