Personality-Based Emotion Recognition Using EEG Signals with a CNN-LSTM Network
The accurate detection of emotions has significant implications in healthcare, psychology, and human–computer interaction. Integrating personality information into emotion recognition can enhance its utility in various applications. The present study introduces a novel deep learning approach to emot...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-06-01
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| Series: | Brain Sciences |
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| Online Access: | https://www.mdpi.com/2076-3425/13/6/947 |
| _version_ | 1797595710379524096 |
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| author | Mohammad Saleh Khajeh Hosseini Seyed Mohammad Firoozabadi Kambiz Badie Parviz Azadfallah |
| author_facet | Mohammad Saleh Khajeh Hosseini Seyed Mohammad Firoozabadi Kambiz Badie Parviz Azadfallah |
| author_sort | Mohammad Saleh Khajeh Hosseini |
| collection | DOAJ |
| description | The accurate detection of emotions has significant implications in healthcare, psychology, and human–computer interaction. Integrating personality information into emotion recognition can enhance its utility in various applications. The present study introduces a novel deep learning approach to emotion recognition, which utilizes electroencephalography (EEG) signals and the Big Five personality traits. The study recruited 60 participants and recorded their EEG data while they viewed unique sequence stimuli designed to effectively capture the dynamic nature of human emotions and personality traits. A pre-trained convolutional neural network (CNN) was used to extract emotion-related features from the raw EEG data. Additionally, a long short-term memory (LSTM) network was used to extract features related to the Big Five personality traits. The network was able to accurately predict personality traits from EEG data. The extracted features were subsequently used in a novel network to predict emotional states within the arousal and valence dimensions. The experimental results showed that the proposed classifier outperformed common classifiers, with a high accuracy of 93.97%. The findings suggest that incorporating personality traits as features in the designed network, for emotion recognition, leads to higher accuracy, highlighting the significance of examining these traits in the analysis of emotions. |
| first_indexed | 2024-03-11T02:41:12Z |
| format | Article |
| id | doaj.art-e247256900c7428ca53c2ece79622b3c |
| institution | Directory Open Access Journal |
| issn | 2076-3425 |
| language | English |
| last_indexed | 2024-03-11T02:41:12Z |
| publishDate | 2023-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Brain Sciences |
| spelling | doaj.art-e247256900c7428ca53c2ece79622b3c2023-11-18T09:36:46ZengMDPI AGBrain Sciences2076-34252023-06-0113694710.3390/brainsci13060947Personality-Based Emotion Recognition Using EEG Signals with a CNN-LSTM NetworkMohammad Saleh Khajeh Hosseini0Seyed Mohammad Firoozabadi1Kambiz Badie2Parviz Azadfallah3Department of Biomedical Engineering, Science and Research Branche, Islamic Azad University, Tehran 14778-93855, IranDepartment of Medical Physics, Faculty of Medicine, Tarbiat Modares University, Tehran 14117-13116, IranContent & E-Services Research Group, IT Research Faculty, ICT Research Institute, Tehran 14399-55471, IranDepartment of Psychology, Faculty of Humanities, Tarbiat Modares University, Tehran 14117-13116, IranThe accurate detection of emotions has significant implications in healthcare, psychology, and human–computer interaction. Integrating personality information into emotion recognition can enhance its utility in various applications. The present study introduces a novel deep learning approach to emotion recognition, which utilizes electroencephalography (EEG) signals and the Big Five personality traits. The study recruited 60 participants and recorded their EEG data while they viewed unique sequence stimuli designed to effectively capture the dynamic nature of human emotions and personality traits. A pre-trained convolutional neural network (CNN) was used to extract emotion-related features from the raw EEG data. Additionally, a long short-term memory (LSTM) network was used to extract features related to the Big Five personality traits. The network was able to accurately predict personality traits from EEG data. The extracted features were subsequently used in a novel network to predict emotional states within the arousal and valence dimensions. The experimental results showed that the proposed classifier outperformed common classifiers, with a high accuracy of 93.97%. The findings suggest that incorporating personality traits as features in the designed network, for emotion recognition, leads to higher accuracy, highlighting the significance of examining these traits in the analysis of emotions.https://www.mdpi.com/2076-3425/13/6/947emotion recognitionpersonality traitsdeep neural network |
| spellingShingle | Mohammad Saleh Khajeh Hosseini Seyed Mohammad Firoozabadi Kambiz Badie Parviz Azadfallah Personality-Based Emotion Recognition Using EEG Signals with a CNN-LSTM Network Brain Sciences emotion recognition personality traits deep neural network |
| title | Personality-Based Emotion Recognition Using EEG Signals with a CNN-LSTM Network |
| title_full | Personality-Based Emotion Recognition Using EEG Signals with a CNN-LSTM Network |
| title_fullStr | Personality-Based Emotion Recognition Using EEG Signals with a CNN-LSTM Network |
| title_full_unstemmed | Personality-Based Emotion Recognition Using EEG Signals with a CNN-LSTM Network |
| title_short | Personality-Based Emotion Recognition Using EEG Signals with a CNN-LSTM Network |
| title_sort | personality based emotion recognition using eeg signals with a cnn lstm network |
| topic | emotion recognition personality traits deep neural network |
| url | https://www.mdpi.com/2076-3425/13/6/947 |
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