EEG-based emotion recognition using hybrid CNN and LSTM classification
Emotions are a mental state that is accompanied by a distinct physiologic rhythm, as well as physical, behavioral, and mental changes. In the latest days, physiological activity has been used to study emotional reactions. This study describes the electroencephalography (EEG) signals, the brain wave...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-10-01
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| Series: | Frontiers in Computational Neuroscience |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fncom.2022.1019776/full |
| _version_ | 1797996308878852096 |
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| author | Bhuvaneshwari Chakravarthi Sin-Chun Ng M. R. Ezilarasan Man-Fai Leung |
| author_facet | Bhuvaneshwari Chakravarthi Sin-Chun Ng M. R. Ezilarasan Man-Fai Leung |
| author_sort | Bhuvaneshwari Chakravarthi |
| collection | DOAJ |
| description | Emotions are a mental state that is accompanied by a distinct physiologic rhythm, as well as physical, behavioral, and mental changes. In the latest days, physiological activity has been used to study emotional reactions. This study describes the electroencephalography (EEG) signals, the brain wave pattern, and emotion analysis all of these are interrelated and based on the consequences of human behavior and Post-Traumatic Stress Disorder (PTSD). Post-traumatic stress disorder effects for long-term illness are associated with considerable suffering, impairment, and social/emotional impairment. PTSD is connected to subcortical responses to injury memories, thoughts, and emotions and alterations in brain circuitry. Predominantly EEG signals are the way of examining the electrical potential of the human feelings cum expression for every changing phenomenon that the individual faces. When going through literature there are some lacunae while analyzing emotions. There exist some reliability issues and also masking of real emotional behavior by the victims. Keeping this research gap and hindrance faced by the previous researchers the present study aims to fulfill the requirements, the efforts can be made to overcome this problem, and the proposed automated CNN-LSTM with ResNet-152 algorithm. Compared with the existing techniques, the proposed techniques achieved a higher level of accuracy of 98% by applying the hybrid deep learning algorithm. |
| first_indexed | 2024-04-11T10:15:20Z |
| format | Article |
| id | doaj.art-bad3da2d21ec4831b38dcc556a1c78d6 |
| institution | Directory Open Access Journal |
| issn | 1662-5188 |
| language | English |
| last_indexed | 2024-04-11T10:15:20Z |
| publishDate | 2022-10-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Computational Neuroscience |
| spelling | doaj.art-bad3da2d21ec4831b38dcc556a1c78d62022-12-22T04:29:59ZengFrontiers Media S.A.Frontiers in Computational Neuroscience1662-51882022-10-011610.3389/fncom.2022.10197761019776EEG-based emotion recognition using hybrid CNN and LSTM classificationBhuvaneshwari Chakravarthi0Sin-Chun Ng1M. R. Ezilarasan2Man-Fai Leung3School of Computing and Information Science, Faculty of Science and Engineering, Anglia Ruskin University, Cambridge, United KingdomSchool of Computing and Information Science, Faculty of Science and Engineering, Anglia Ruskin University, Cambridge, United KingdomDepartment of Electronics and Communication Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai, IndiaSchool of Computing and Information Science, Faculty of Science and Engineering, Anglia Ruskin University, Cambridge, United KingdomEmotions are a mental state that is accompanied by a distinct physiologic rhythm, as well as physical, behavioral, and mental changes. In the latest days, physiological activity has been used to study emotional reactions. This study describes the electroencephalography (EEG) signals, the brain wave pattern, and emotion analysis all of these are interrelated and based on the consequences of human behavior and Post-Traumatic Stress Disorder (PTSD). Post-traumatic stress disorder effects for long-term illness are associated with considerable suffering, impairment, and social/emotional impairment. PTSD is connected to subcortical responses to injury memories, thoughts, and emotions and alterations in brain circuitry. Predominantly EEG signals are the way of examining the electrical potential of the human feelings cum expression for every changing phenomenon that the individual faces. When going through literature there are some lacunae while analyzing emotions. There exist some reliability issues and also masking of real emotional behavior by the victims. Keeping this research gap and hindrance faced by the previous researchers the present study aims to fulfill the requirements, the efforts can be made to overcome this problem, and the proposed automated CNN-LSTM with ResNet-152 algorithm. Compared with the existing techniques, the proposed techniques achieved a higher level of accuracy of 98% by applying the hybrid deep learning algorithm.https://www.frontiersin.org/articles/10.3389/fncom.2022.1019776/fulldeep learningelectroencephalographyemotion recognitionneural networksmachine learning |
| spellingShingle | Bhuvaneshwari Chakravarthi Sin-Chun Ng M. R. Ezilarasan Man-Fai Leung EEG-based emotion recognition using hybrid CNN and LSTM classification Frontiers in Computational Neuroscience deep learning electroencephalography emotion recognition neural networks machine learning |
| title | EEG-based emotion recognition using hybrid CNN and LSTM classification |
| title_full | EEG-based emotion recognition using hybrid CNN and LSTM classification |
| title_fullStr | EEG-based emotion recognition using hybrid CNN and LSTM classification |
| title_full_unstemmed | EEG-based emotion recognition using hybrid CNN and LSTM classification |
| title_short | EEG-based emotion recognition using hybrid CNN and LSTM classification |
| title_sort | eeg based emotion recognition using hybrid cnn and lstm classification |
| topic | deep learning electroencephalography emotion recognition neural networks machine learning |
| url | https://www.frontiersin.org/articles/10.3389/fncom.2022.1019776/full |
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