A Pre-Activation Residual Convolutional Network With Attention Modules for High-Resolution Segmented EEG Emotion Recognition
Emotion recognition based on electroencephalography (EEG) signals has attracted considerable research interest over the past few years and several potential applications have been proposed such as enhancing human-computer interaction, improving mental health diagnosis, and fine-tuning the customer e...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10843693/ |
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| author | Ioannis Charalampous Christos Mavrokefalidis Kostas Berberidis |
| author_facet | Ioannis Charalampous Christos Mavrokefalidis Kostas Berberidis |
| author_sort | Ioannis Charalampous |
| collection | DOAJ |
| description | Emotion recognition based on electroencephalography (EEG) signals has attracted considerable research interest over the past few years and several potential applications have been proposed such as enhancing human-computer interaction, improving mental health diagnosis, and fine-tuning the customer experience at the marketing level. This paper introduces a novel model, called Pre-Activation Residual Convolutional Network with Attention Modules (PRCN-AM), designed to enhance the accuracy and robustness of emotion recognition based on EEG signals. PRCN-AM combines residual convolutional layers with pre-activation and attention modules to effectively capture and analyze the complex spatial-temporal patterns inherent in EEG signals. Two experimental procedures, namely, subject-dependent and subject-independent, were conducted and different time segmentations on the preprocessing stage were tested. The suggested exploitation of the temporal dynamics of the EEG signals in emotion recognition turns out to be useful, as classification accuracies of up to 99.51% and 97.51% on SEED and SEED-IV datasets have been achieved, respectively, thus, outperforming the current state-of-the-art models. |
| format | Article |
| id | doaj-art-f52aa64cf4034d98b44f5fe3cfff94d1 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-f52aa64cf4034d98b44f5fe3cfff94d12025-01-29T00:01:06ZengIEEEIEEE Access2169-35362025-01-0113163031631310.1109/ACCESS.2025.353056710843693A Pre-Activation Residual Convolutional Network With Attention Modules for High-Resolution Segmented EEG Emotion RecognitionIoannis Charalampous0https://orcid.org/0009-0003-5020-030XChristos Mavrokefalidis1https://orcid.org/0000-0002-0131-9633Kostas Berberidis2https://orcid.org/0000-0003-2175-9043Department of Computer Engineering and Informatics, University of Patras, Patras, GreeceDepartment of Computer Engineering and Informatics, University of Patras, Patras, GreeceDepartment of Computer Engineering and Informatics, University of Patras, Patras, GreeceEmotion recognition based on electroencephalography (EEG) signals has attracted considerable research interest over the past few years and several potential applications have been proposed such as enhancing human-computer interaction, improving mental health diagnosis, and fine-tuning the customer experience at the marketing level. This paper introduces a novel model, called Pre-Activation Residual Convolutional Network with Attention Modules (PRCN-AM), designed to enhance the accuracy and robustness of emotion recognition based on EEG signals. PRCN-AM combines residual convolutional layers with pre-activation and attention modules to effectively capture and analyze the complex spatial-temporal patterns inherent in EEG signals. Two experimental procedures, namely, subject-dependent and subject-independent, were conducted and different time segmentations on the preprocessing stage were tested. The suggested exploitation of the temporal dynamics of the EEG signals in emotion recognition turns out to be useful, as classification accuracies of up to 99.51% and 97.51% on SEED and SEED-IV datasets have been achieved, respectively, thus, outperforming the current state-of-the-art models.https://ieeexplore.ieee.org/document/10843693/EEGemotion recognitionresidual convolutional networkattention modulespre-activationsegmentation |
| spellingShingle | Ioannis Charalampous Christos Mavrokefalidis Kostas Berberidis A Pre-Activation Residual Convolutional Network With Attention Modules for High-Resolution Segmented EEG Emotion Recognition IEEE Access EEG emotion recognition residual convolutional network attention modules pre-activation segmentation |
| title | A Pre-Activation Residual Convolutional Network With Attention Modules for High-Resolution Segmented EEG Emotion Recognition |
| title_full | A Pre-Activation Residual Convolutional Network With Attention Modules for High-Resolution Segmented EEG Emotion Recognition |
| title_fullStr | A Pre-Activation Residual Convolutional Network With Attention Modules for High-Resolution Segmented EEG Emotion Recognition |
| title_full_unstemmed | A Pre-Activation Residual Convolutional Network With Attention Modules for High-Resolution Segmented EEG Emotion Recognition |
| title_short | A Pre-Activation Residual Convolutional Network With Attention Modules for High-Resolution Segmented EEG Emotion Recognition |
| title_sort | pre activation residual convolutional network with attention modules for high resolution segmented eeg emotion recognition |
| topic | EEG emotion recognition residual convolutional network attention modules pre-activation segmentation |
| url | https://ieeexplore.ieee.org/document/10843693/ |
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