Package: eeggan
EEG-GANWe 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 our documentationhereFeel 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 modulesmodule load python/3.9.0
module load gcc/10.2
module load cuda/11.7.1
module load cudnn/8.2.0Create and activate virtual environmentpython3 -m venv myVirtualEnv

source ./myVirtualEnv/bin/activateInstall packagesNote that the following packages are all the same as the requirements.txt except for torch, torchvision, torchaudio, torchsummary. TODO: Add new requirements.txtpip3 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==1.1.2Run gans trainingThat should be all and now you should get no errors when running:python gan_training_main.py ddpLICENSECopyright 2023, Brown University, Providence, RI.All Rights ReservedPermission 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.2.0Licensed 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 athttp://www.apache.org/licenses/LICENSE-2.0Unless 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.