Customized 2D CNN Model for the Automatic Emotion Recognition Based on EEG Signals
Automatic emotion recognition from electroencephalogram (EEG) signals can be considered as the main component of brain–computer interface (BCI) systems. In the previous years, many researchers in this direction have presented various algorithms for the automatic classification of emotions from EEG s...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-05-01
|
| Series: | Electronics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2079-9292/12/10/2232 |
| _version_ | 1797600285556736000 |
|---|---|
| author | Farzad Baradaran Ali Farzan Sebelan Danishvar Sobhan Sheykhivand |
| author_facet | Farzad Baradaran Ali Farzan Sebelan Danishvar Sobhan Sheykhivand |
| author_sort | Farzad Baradaran |
| collection | DOAJ |
| description | Automatic emotion recognition from electroencephalogram (EEG) signals can be considered as the main component of brain–computer interface (BCI) systems. In the previous years, many researchers in this direction have presented various algorithms for the automatic classification of emotions from EEG signals, and they have achieved promising results; however, lack of stability, high error, and low accuracy are still considered as the central gaps in this research. For this purpose, obtaining a model with the precondition of stability, high accuracy, and low error is considered essential for the automatic classification of emotions. In this research, a model based on Deep Convolutional Neural Networks (DCNNs) is presented, which can classify three positive, negative, and neutral emotions from EEG signals based on musical stimuli with high reliability. For this purpose, a comprehensive database of EEG signals has been collected while volunteers were listening to positive and negative music in order to stimulate the emotional state. The architecture of the proposed model consists of a combination of six convolutional layers and two fully connected layers. In this research, different feature learning and hand-crafted feature selection/extraction algorithms were investigated and compared with each other in order to classify emotions. The proposed model for the classification of two classes (positive and negative) and three classes (positive, neutral, and negative) of emotions had 98% and 96% accuracy, respectively, which is very promising compared with the results of previous research. In order to evaluate more fully, the proposed model was also investigated in noisy environments; with a wide range of different SNRs, the classification accuracy was still greater than 90%. Due to the high performance of the proposed model, it can be used in brain–computer user environments. |
| first_indexed | 2024-03-11T03:46:20Z |
| format | Article |
| id | doaj.art-0a889f04e3684b6384a0bc5586700c3c |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-11T03:46:20Z |
| publishDate | 2023-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-0a889f04e3684b6384a0bc5586700c3c2023-11-18T01:09:34ZengMDPI AGElectronics2079-92922023-05-011210223210.3390/electronics12102232Customized 2D CNN Model for the Automatic Emotion Recognition Based on EEG SignalsFarzad Baradaran0Ali Farzan1Sebelan Danishvar2Sobhan Sheykhivand3Department of Computer Engineering, Shabestar Branch, Islamic Azad University, Shabestar 53816-37181, IranDepartment of Computer Engineering, Shabestar Branch, Islamic Azad University, Shabestar 53816-37181, IranCollege of Engineering, Design and Physical Sciences, Brunel University London, Uxbridge UB8 3PH, UKDepartment of Biomedical Engineering, University of Bonab, Bonab 55517-61167, IranAutomatic emotion recognition from electroencephalogram (EEG) signals can be considered as the main component of brain–computer interface (BCI) systems. In the previous years, many researchers in this direction have presented various algorithms for the automatic classification of emotions from EEG signals, and they have achieved promising results; however, lack of stability, high error, and low accuracy are still considered as the central gaps in this research. For this purpose, obtaining a model with the precondition of stability, high accuracy, and low error is considered essential for the automatic classification of emotions. In this research, a model based on Deep Convolutional Neural Networks (DCNNs) is presented, which can classify three positive, negative, and neutral emotions from EEG signals based on musical stimuli with high reliability. For this purpose, a comprehensive database of EEG signals has been collected while volunteers were listening to positive and negative music in order to stimulate the emotional state. The architecture of the proposed model consists of a combination of six convolutional layers and two fully connected layers. In this research, different feature learning and hand-crafted feature selection/extraction algorithms were investigated and compared with each other in order to classify emotions. The proposed model for the classification of two classes (positive and negative) and three classes (positive, neutral, and negative) of emotions had 98% and 96% accuracy, respectively, which is very promising compared with the results of previous research. In order to evaluate more fully, the proposed model was also investigated in noisy environments; with a wide range of different SNRs, the classification accuracy was still greater than 90%. Due to the high performance of the proposed model, it can be used in brain–computer user environments.https://www.mdpi.com/2079-9292/12/10/2232emotion recognitiondeep learningEEGmusicCNN |
| spellingShingle | Farzad Baradaran Ali Farzan Sebelan Danishvar Sobhan Sheykhivand Customized 2D CNN Model for the Automatic Emotion Recognition Based on EEG Signals Electronics emotion recognition deep learning EEG music CNN |
| title | Customized 2D CNN Model for the Automatic Emotion Recognition Based on EEG Signals |
| title_full | Customized 2D CNN Model for the Automatic Emotion Recognition Based on EEG Signals |
| title_fullStr | Customized 2D CNN Model for the Automatic Emotion Recognition Based on EEG Signals |
| title_full_unstemmed | Customized 2D CNN Model for the Automatic Emotion Recognition Based on EEG Signals |
| title_short | Customized 2D CNN Model for the Automatic Emotion Recognition Based on EEG Signals |
| title_sort | customized 2d cnn model for the automatic emotion recognition based on eeg signals |
| topic | emotion recognition deep learning EEG music CNN |
| url | https://www.mdpi.com/2079-9292/12/10/2232 |
| work_keys_str_mv | AT farzadbaradaran customized2dcnnmodelfortheautomaticemotionrecognitionbasedoneegsignals AT alifarzan customized2dcnnmodelfortheautomaticemotionrecognitionbasedoneegsignals AT sebelandanishvar customized2dcnnmodelfortheautomaticemotionrecognitionbasedoneegsignals AT sobhansheykhivand customized2dcnnmodelfortheautomaticemotionrecognitionbasedoneegsignals |