Video Tutorials: Depression Detection using EEG signals by Graphical Deep learning

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

abhishek gupta

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