Subject independent emotion recognition from EEG using VMD and deep learning
Emotion recognition from Electroencephalography (EEG) is proved to be a good choice as it cannot be mimicked like speech signals or facial expressions. EEG signals of emotions are not unique and it varies from person to person as each one has different emotional responses to the same stimuli. Thus E...
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| Format: | Article |
| Language: | English |
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Elsevier
2022-05-01
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| Series: | Journal of King Saud University: Computer and Information Sciences |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1319157819309991 |
| _version_ | 1811308926563516416 |
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| author | Pallavi Pandey K.R. Seeja |
| author_facet | Pallavi Pandey K.R. Seeja |
| author_sort | Pallavi Pandey |
| collection | DOAJ |
| description | Emotion recognition from Electroencephalography (EEG) is proved to be a good choice as it cannot be mimicked like speech signals or facial expressions. EEG signals of emotions are not unique and it varies from person to person as each one has different emotional responses to the same stimuli. Thus EEG signals are subject dependent and proved to be effective for subject dependent emotion recognition. However, subject independent emotion recognition plays an important role in situations like emotion recognition from paralyzed or burnt face, where EEG of emotions of the subjects before the incidents are not available to build the emotion recognition model. Hence there is a need to identify common EEG patterns corresponds to each emotion independent of the subjects. In this paper, a subject independent emotion recognition technique is proposed from EEG signals using Variational Mode Decomposition (VMD) as a feature extraction technique and Deep Neural Network as the classifier. The performance evaluation of the proposed method with the benchmark DEAP dataset shows that the combination of VMD and Deep Neural Network performs better compared to the state of the art techniques in subject-independent emotion recognition from EEG. |
| first_indexed | 2024-04-13T09:33:12Z |
| format | Article |
| id | doaj.art-6695e677725642df8545e1e038fcea6b |
| institution | Directory Open Access Journal |
| issn | 1319-1578 |
| language | English |
| last_indexed | 2024-04-13T09:33:12Z |
| publishDate | 2022-05-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of King Saud University: Computer and Information Sciences |
| spelling | doaj.art-6695e677725642df8545e1e038fcea6b2022-12-22T02:52:11ZengElsevierJournal of King Saud University: Computer and Information Sciences1319-15782022-05-0134517301738Subject independent emotion recognition from EEG using VMD and deep learningPallavi Pandey0K.R. Seeja1Department of Computer Science & Engineering, Indira Gandhi Delhi Technical University for Women, Kashmere Gate, Delhi 110006, IndiaCorresponding author.; Department of Computer Science & Engineering, Indira Gandhi Delhi Technical University for Women, Kashmere Gate, Delhi 110006, IndiaEmotion recognition from Electroencephalography (EEG) is proved to be a good choice as it cannot be mimicked like speech signals or facial expressions. EEG signals of emotions are not unique and it varies from person to person as each one has different emotional responses to the same stimuli. Thus EEG signals are subject dependent and proved to be effective for subject dependent emotion recognition. However, subject independent emotion recognition plays an important role in situations like emotion recognition from paralyzed or burnt face, where EEG of emotions of the subjects before the incidents are not available to build the emotion recognition model. Hence there is a need to identify common EEG patterns corresponds to each emotion independent of the subjects. In this paper, a subject independent emotion recognition technique is proposed from EEG signals using Variational Mode Decomposition (VMD) as a feature extraction technique and Deep Neural Network as the classifier. The performance evaluation of the proposed method with the benchmark DEAP dataset shows that the combination of VMD and Deep Neural Network performs better compared to the state of the art techniques in subject-independent emotion recognition from EEG.http://www.sciencedirect.com/science/article/pii/S1319157819309991Variational Mode DecompositionValence-Arousal modelDeep Neural NetworkAffective computingIntrinsic-mode functions |
| spellingShingle | Pallavi Pandey K.R. Seeja Subject independent emotion recognition from EEG using VMD and deep learning Journal of King Saud University: Computer and Information Sciences Variational Mode Decomposition Valence-Arousal model Deep Neural Network Affective computing Intrinsic-mode functions |
| title | Subject independent emotion recognition from EEG using VMD and deep learning |
| title_full | Subject independent emotion recognition from EEG using VMD and deep learning |
| title_fullStr | Subject independent emotion recognition from EEG using VMD and deep learning |
| title_full_unstemmed | Subject independent emotion recognition from EEG using VMD and deep learning |
| title_short | Subject independent emotion recognition from EEG using VMD and deep learning |
| title_sort | subject independent emotion recognition from eeg using vmd and deep learning |
| topic | Variational Mode Decomposition Valence-Arousal model Deep Neural Network Affective computing Intrinsic-mode functions |
| url | http://www.sciencedirect.com/science/article/pii/S1319157819309991 |
| work_keys_str_mv | AT pallavipandey subjectindependentemotionrecognitionfromeegusingvmdanddeeplearning AT krseeja subjectindependentemotionrecognitionfromeegusingvmdanddeeplearning |