We here use Generative Adversarial Networks (GANs) to create trial-level synthetic EEG samples. We can then use these samples as extra data to train whichever classifier we want to use (e.g., Support Vector Machine, Neural Network).
You can find out documentation here
Feel free to contribute!
This method requires a different virtual environment than within the repo. Here are instructions on how to do this using Open on Demand (ood.ccv.brown.edu).
First, start a Virtual Desktop by going to the 'My Interactive Sessions' tab at the top and then selecting Desktop (Advanced). You will then be confronted with a range of fields with defaults. Change 'Partition' to 'GPU' and insert '8' under Num GPUs. You can also change the number of CPUs and RAM ize if you like, but defaults should work. Hit the 'Launch' button at the bottom when you are ready and it will bring you back to your 'My Interactive Sessions' tab with a session for 'Desktop (Advanced)' starting. The session will eventually establish (should not take long) and a 'Launch Desktop (Advanced)' button will appear.
Launching the desktop will take you to a virtual desktop. Open terminal and navigate to where you would like to create your virtual environment. You will then build the environment as such:
Module load cuda/11.8.0-lpttyok
Module load cudnn/8.7.0.84-11.8-lg2dpd5
Module load gcc/10.1.0-mojgbnp
python3 -m venv myVirtualEnv
source ./myVirtualEnv/bin/activate
Note that the following packages are all the same as the requirements.txt except for torch, torchvision, torchaudio, torchsummary. TODO: Add new requirements.txt
pip3 install torch torchvision torchaudio torchsummary
pip install pandas==1.3.4
pip install numpy==1.21.4
pip install matplotlib==3.5.0
pip install scipy==1.8.0
pip install einops==0.4.1
pip install scikit-learn==1.1.2
That should be all and now you should get no errors when running:
python gan_training_main.py ddp
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