https://github.com/autoresearch/eeg-gan
Science Score: 26.0%
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○CITATION.cff file
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✓codemeta.json file
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✓.zenodo.json file
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○DOI references
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○Scientific vocabulary similarity
Low similarity (10.4%) to scientific vocabulary
Repository
Basic Info
- Host: GitHub
- Owner: AutoResearch
- License: other
- Language: Python
- Default Branch: main
- Size: 14.3 GB
Statistics
- Stars: 27
- Watchers: 1
- Forks: 4
- Open Issues: 9
- Releases: 3
Metadata Files
README.md
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).
GANs are machine learning frameworks that consist of two adversarial neural network agents, namely the generator and the discriminator. The generator is trained to create novel samples that are indiscernible from real samples. In the current context, the generator produces realistic continuous EEG activity, conditioned on a set of experimental variables, which contain underlying neural features representative of the outcomes being classified. For example, depression manifests as increased alpha oscillatory activity in the EEG signal, and thus, an ideal generator would produce continuous EEG that includes these alpha signatures. In contrast to the generator, the discriminator determines whether a given sample is real or synthetically produced by the generator. The core insight of GANs is that the generator can effectively learn from the discriminator. Specifically, the generator will consecutively produce more realistic synthetic samples with the goal of “fooling” the discriminator into believing them as real. Once it has achieved realistic samples that the discriminator cannot discern, it can be used to generate synthetic data—or in this context, synthetic EEG data.
You can find our documentation here
Feel free to contribute!
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.
Owner
- Name: Autonomous Empirical Research Initiative
- Login: AutoResearch
- Kind: organization
- Website: www.empiricalresearch.ai
- Repositories: 12
- Profile: https://github.com/AutoResearch
We strive to enhance and accelerate scientific discovery by automating steps in the empirical research process.
GitHub Events
Total
- Issues event: 5
- Watch event: 10
- Issue comment event: 8
- Push event: 13
- Pull request event: 2
- Fork event: 2
Last Year
- Issues event: 5
- Watch event: 10
- Issue comment event: 8
- Push event: 13
- Pull request event: 2
- Fork event: 2
Issues and Pull Requests
Last synced: 10 months ago
All Time
- Total issues: 72
- Total pull requests: 34
- Average time to close issues: 3 months
- Average time to close pull requests: 10 days
- Total issue authors: 6
- Total pull request authors: 2
- Average comments per issue: 1.47
- Average comments per pull request: 0.32
- Merged pull requests: 29
- Bot issues: 0
- Bot pull requests: 0
Past Year
- Issues: 3
- Pull requests: 4
- Average time to close issues: 9 days
- Average time to close pull requests: 6 minutes
- Issue authors: 2
- Pull request authors: 2
- Average comments per issue: 2.67
- Average comments per pull request: 0.5
- Merged pull requests: 3
- Bot issues: 0
- Bot pull requests: 0
Top Authors
Issue Authors
- chadcwilliams (56)
- whyhardt (5)
- TheLemonPig (3)
- Brandoncheaa (1)
- Zhreyu (1)
- thelegitdolt (1)
Pull Request Authors
- chadcwilliams (50)
- TheLemonPig (4)
Top Labels
Issue Labels
Pull Request Labels
Packages
- Total packages: 1
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Total downloads:
- pypi 117 last-month
- Total dependent packages: 0
- Total dependent repositories: 0
- Total versions: 58
- Total maintainers: 1
pypi.org: eeggan
This package uses Generative Adversarial Networks (GANs) to augment EEG data to enhance classification performance.
- Homepage: https://musslick.github.io/AER_website/Research.html
- Documentation: https://autoresearch.github.io/EEG-GAN/
- License: other
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Latest release: 2.0.2
published 12 months ago