TFCNN-BiGRU

版本 1.0.0 (2.6 KB) 作者: Prof. Dr. Essam H Houssein

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

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更新时间 2024/5/3

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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.

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Prof. Dr. Essam H Houssein (2024). TFCNN-BiGRU (https://www.mathworks.com/matlabcentral/fileexchange/165126-tfcnn-bigru), MATLAB Central File Exchange. 检索来源 2024/8/25.