Description
**# In this video tutorials, you will find follwing content:**
**Module 1: Data Discussion for Depression Recognition**
* A broad discussion of the objective
* EEG Data Discussion and Sharing
* EEG Data noise filtering Code implementation and visualization
* Restructuring of the data
**Module2: EEG Data filtering **
* Continuing Restructuring of the Data and visualization
* EEG Signals channels into graphical nodes
* Adjacency matrix implementation and visualization
* EEG Signal as a graphical structure
**Module 3: PyTorch Implementation of Graphical Deep Learning**
* Introduction to PyTorch package for advanced deep learning
* Understanding of Graphical Convolution Network (GCN)
* Implementation of GCN for EEG signal
* Visualization of the outcome of signal features through GCN
**Module 4: Spatiotemporal Features Extraction from EEG**
* Sequentially implementation of Gated Recurrent Unit (GRU) in PyTorch
* Training the GCN and GRU collectively for EEG feature extraction
* Visualization of features
* Masking of the features to remove redundant information
**Module 5: Redundant features Removal and Model training**
* Continue Masking of features to remove redundant information
* Model Training
* Graph max-pooling layer implementation for the classification of depression through EEG
* Training the complete model and parameters evaluation
**Module 6: Future Scope and Contribution Discussion by Experts**
* Plot all visualizations
* Expert lecture from the reputed faculty of the University to discuss the future scope
* Expert lecture from the Industrial Expert in the same field
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Dr. Abhishek Gupta
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