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TFCNN-BiGRU with self-attention mechanism for automatic human emotion recognition using multi-channel EEG data

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Abstract

Electroencephalograms (EEG)-based technology for recognizing emotions has attracted a lot of interest lately. However, there is still work to be done on the efficient fusion of different temporal and spatial features of EEG signals to improve performance in emotion recognition. Therefore, this study suggests a new deep learning architecture that combines a time–frequency convolutional neural network (TFCNN), a bidirectional gated recurrent unit (BiGRU), and a self-attention mechanism (SAM) to categorize emotions based on EEG signals and automatically extract features. The first step is to use the continuous wavelet transform (CWT), which responds more readily to temporal frequency variations within EEG recordings, as a layer inside the convolutional layers, to create 2D scalogram images from EEG signals for time series and spatial representation learning. Second, to encode more discriminative features representing emotions, two-dimensional (2D)-CNN, BiGRU, and SAM are trained on these scalograms simultaneously to capture the appropriate information from spatial, local, temporal, and global aspects. Finally, EEG signals are categorized into several emotional states. This network can learn the temporal dependencies of EEG emotion signals with BiGRU, extract local spatial features with TFCNN, and improve recognition accuracy with SAM, which is applied to explore global signal correlations by reassigning weights to emotion features. Using the SEED and GAMEEMO datasets, the suggested strategy was evaluated on three different classification tasks: one with two target classes (positive and negative), one with three target classes (positive, neutral, and negative), and one with four target classes (boring, calm, horror, and funny). Based on the comprehensive results of the experiments, the suggested approach achieved a 93.1%, 96.2%, and 92.9% emotion detection accuracy in two, three, and four classes, respectively, which are 0.281%, 1.98%, and 2.57% higher than the existing approaches working on the same datasets for different subjects, respectively. The open source codes are available at https://www.mathworks.com/matlabcentral/fileexchange/165126-tfcnn-bigru

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Acknowledgements

The authors would like to express their grateful to Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2024R330), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.

Funding

This research was funded by Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2024R330), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia

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Authors and Affiliations

  1. Faculty of Computers and Information, Minia University, Minia, Egypt

    Essam H. Houssein & Asmaa Hammad

  2. Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, 11671, Riyadh, Saudi Arabia

    Nagwan Abdel Samee & Abdelmgeid A. Ali

  3. Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, 11671, Riyadh, Saudi Arabia

    Manal Abdullah Alohali

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  1. Essam H. Houssein
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Contributions

Essam H. Houssein: Supervising, Software, Methodology, Conceptualization, Formal analysis, Investigation, Visualization, Writing-review and editing. Asmaa Hammad: Formal analysis, Software, Methodology, visualization, resources, data curation, writing original draft, Writing-review and editing. Nagwan Abdel Samee: Formal analysis, Investigation, visualization, resources, Writing-review and editing. Manal Abdullah Alohali: Formal analysis, Investigation, visualization, resources, Writing-review and editing. Abdelmgeid A. Ali: Supervision. All authors read and approved the final article.

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Correspondence to Nagwan Abdel Samee.

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Houssein, E.H., Hammad, A., Samee, N.A. et al. TFCNN-BiGRU with self-attention mechanism for automatic human emotion recognition using multi-channel EEG data. Cluster Comput 27, 14365–14385 (2024). https://doi.org/10.1007/s10586-024-04590-5

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