TFCNN-BiGRU

TFCNN-BiGRU with self-attention mechanism for automatic human Emotion Recognition using Multi-Channel EEG Data
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Updated 3 May 2024

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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.

Cite As

Prof. Dr. Essam H Houssein (2024). TFCNN-BiGRU (https://www.mathworks.com/matlabcentral/fileexchange/165126-tfcnn-bigru), MATLAB Central File Exchange. Retrieved .

MATLAB Release Compatibility
Created with R2024a
Compatible with any release
Platform Compatibility
Windows macOS Linux
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Acknowledgements

Inspired by: EEG SIGNAL ANALYSIS , Deep Learning Tutorial Series

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TFCNN_BiGRU_SAM

Version Published Release Notes
1.0.0