eeggan 2.0.0a19

This package uses Generative Adversarial Networks (GANs) to augment EEG data to enhance classification performance.

EEG-GAN

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!

Running GANs on Brown's Oscar Cluster with 8GPUs (internal information for current developpers)

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:

Load modules

Module load cuda/11.8.0-lpttyok
Module load cudnn/8.7.0.84-11.8-lg2dpd5
Module load gcc/10.1.0-mojgbnp

Create and activate virtual environment

python3 -m venv myVirtualEnv

source ./myVirtualEnv/bin/activate

Install packages

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

Run gans training

That should be all and now you should get no errors when running:

python gan_training_main.py ddp

LICENSE

Copyright 2023, Brown University, Providence, RI.

                    All Rights Reserved

Permission to use, copy, modify, and distribute this software and its documentation for any purpose other than its incorporation into a commercial product or service is hereby granted without fee, provided that the above copyright notice appear in all copies and that both that copyright notice and this permission notice appear in supporting documentation, and that the name of Brown University not be used in advertising or publicity pertaining to distribution of the software without specific, written prior permission.

BROWN UNIVERSITY DISCLAIMS ALL WARRANTIES WITH REGARD TO THIS SOFTWARE, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR ANY PARTICULAR PURPOSE. IN NO EVENT SHALL BROWN UNIVERSITY BE LIABLE FOR ANY SPECIAL, INDIRECT OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE OF THIS SOFTWARE.

The TTS-GAN package is provided under the Apache license v.2.0

Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at

http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.