IDEA: Intellect database for emotion analysis using EEG signal
Emotion recognition using Electroencephalography (EEG) is a convenient and reliable technique. EEG based emotion detection study can find its application in various fields such as defense, aerospace, medical, and many more. This analysis helps to understand the emotional state of mind. There are two...
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| Format: | Article |
| Language: | English |
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Springer
2022-07-01
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| Series: | Journal of King Saud University: Computer and Information Sciences |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1319157820304870 |
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| author | Vaishali M. Joshi Rajesh B. Ghongade |
| author_facet | Vaishali M. Joshi Rajesh B. Ghongade |
| author_sort | Vaishali M. Joshi |
| collection | DOAJ |
| description | Emotion recognition using Electroencephalography (EEG) is a convenient and reliable technique. EEG based emotion detection study can find its application in various fields such as defense, aerospace, medical, and many more. This analysis helps to understand the emotional state of mind. There are two approaches to study EEG analysis known as subject dependent and independent. In this paper, Modified Differential Entropy (MD-DE) feature extractor is proposed to detect nonlinearity and non-Gaussianity of the EEG signal. The paper adopts both approaches by conducting an EEG analysis on own generated database named as ‘IDEA- Intellect Database for Emotion Analysis’ on 14 subjects. In this work, bidirectional long short-term memory (BiLSTM) network and multilayer perceptron (MLP) network is used to classify emotional state of mind of the subjects. On the ‘IDEA’ database, subject dependent average accuracy achieved is in the order of 98.5% and for subject independent, 88.57%. To reaffirm the improvement in accuracy level, a new approach of Modified Differential Entropy and BiLSTM network is applied on the openly available SEED and DEAP database as well. This experiment established that the average accuracy of emotion detection using MD-DE and BiLSTM network is better than the established methods. |
| format | Article |
| id | doaj-art-696bce5c0cd74a268286e73efefedc69 |
| institution | Kabale University |
| issn | 1319-1578 |
| language | English |
| publishDate | 2022-07-01 |
| publisher | Springer |
| record_format | Article |
| series | Journal of King Saud University: Computer and Information Sciences |
| spelling | doaj-art-696bce5c0cd74a268286e73efefedc692025-08-20T03:48:36ZengSpringerJournal of King Saud University: Computer and Information Sciences1319-15782022-07-013474433444710.1016/j.jksuci.2020.10.007IDEA: Intellect database for emotion analysis using EEG signalVaishali M. Joshi0Rajesh B. Ghongade1Corresponding author.; Electronics Engineering, Bharati Vidyapeeth (Deemed to be University) College of Engineering, Pune 411043, Maharashtra, IndiaElectronics Engineering, Bharati Vidyapeeth (Deemed to be University) College of Engineering, Pune 411043, Maharashtra, IndiaEmotion recognition using Electroencephalography (EEG) is a convenient and reliable technique. EEG based emotion detection study can find its application in various fields such as defense, aerospace, medical, and many more. This analysis helps to understand the emotional state of mind. There are two approaches to study EEG analysis known as subject dependent and independent. In this paper, Modified Differential Entropy (MD-DE) feature extractor is proposed to detect nonlinearity and non-Gaussianity of the EEG signal. The paper adopts both approaches by conducting an EEG analysis on own generated database named as ‘IDEA- Intellect Database for Emotion Analysis’ on 14 subjects. In this work, bidirectional long short-term memory (BiLSTM) network and multilayer perceptron (MLP) network is used to classify emotional state of mind of the subjects. On the ‘IDEA’ database, subject dependent average accuracy achieved is in the order of 98.5% and for subject independent, 88.57%. To reaffirm the improvement in accuracy level, a new approach of Modified Differential Entropy and BiLSTM network is applied on the openly available SEED and DEAP database as well. This experiment established that the average accuracy of emotion detection using MD-DE and BiLSTM network is better than the established methods.http://www.sciencedirect.com/science/article/pii/S1319157820304870EmotionElectroencephalographyModified Differential EntropyBidirectional long short-term memory |
| spellingShingle | Vaishali M. Joshi Rajesh B. Ghongade IDEA: Intellect database for emotion analysis using EEG signal Journal of King Saud University: Computer and Information Sciences Emotion Electroencephalography Modified Differential Entropy Bidirectional long short-term memory |
| title | IDEA: Intellect database for emotion analysis using EEG signal |
| title_full | IDEA: Intellect database for emotion analysis using EEG signal |
| title_fullStr | IDEA: Intellect database for emotion analysis using EEG signal |
| title_full_unstemmed | IDEA: Intellect database for emotion analysis using EEG signal |
| title_short | IDEA: Intellect database for emotion analysis using EEG signal |
| title_sort | idea intellect database for emotion analysis using eeg signal |
| topic | Emotion Electroencephalography Modified Differential Entropy Bidirectional long short-term memory |
| url | http://www.sciencedirect.com/science/article/pii/S1319157820304870 |
| work_keys_str_mv | AT vaishalimjoshi ideaintellectdatabaseforemotionanalysisusingeegsignal AT rajeshbghongade ideaintellectdatabaseforemotionanalysisusingeegsignal |