BCI Image EEG Generator (in progress)

About The Project

The aim of this project is to generate images from human brain based on EEG signal. In order to do this task author build generative neural network architecture called Variational Autoencoder (VAE) . For this particular task author get data throught mobile Brain-Computer-Interface of EMOTIV company in order to get data from human brain.

Getting Started

This is an example of how you may give instructions on setting up your project locally. To get a local copy up and running follow these simple example steps.

Install:

git clone "https://github.com/MikeG27/BCI_Image_EEG_Generator.git"
cd BCI_Image_EEG_Generator
make create_environment
conda activate eeg2image
pip install -r requirements.txt
make test_environment

Usage

Use this space to show useful examples of how a project can be used. Additional screenshots, code examples and demos work well in this space. You may also link to more resources.

Pipeline

1. Download data
2. Run analytics
3. Build features
4. Train model
5. Evaluate model

Run pipeline

To run the execution pipeline type in terminal :

dvc repro .dvc_files/train_models.dvc
or 
make run_experiment

Visualize pipeline stages

dvc pipeline show --ascii .dvc_files/test_model.dvc

Visualize pipeline commands

dvc pipeline show --ascii .dvc_files/test_model.dvc --commands

Show model metrics

dvc metrics show

Project Organization

├── LICENSE
├── Makefile           <- Makefile with commands like `make data` or `make train`
├── README.md          <- The top-level README for developers using this project.
├── data
│   ├── external       <- Data from third party sources.
│   ├── interim        <- Intermediate data that has been transformed.
│   ├── processed      <- The final, canonical data sets for modeling.
│   └── raw            <- The original, immutable data dump.
│
├── docs               <- A default Sphinx project; see sphinx-doc.org for details
│
├── models             <- Trained and serialized models, model predictions, or model summaries
│
├── notebooks          <- Jupyter notebooks. Naming convention is a number (for ordering),
│                         the creator's initials, and a short `-` delimited description, e.g.
│                         `1.0-jqp-initial-data-exploration`.
│
├── references         <- Data dictionaries, manuals, and all other explanatory materials.
│
├── reports            <- Generated analysis as HTML, PDF, LaTeX, etc.
│   └── figures        <- Generated graphics and figures to be used in reporting
│
├── requirements.txt   <- The requirements file for reproducing the analysis environment, e.g.
│                         generated with `pip freeze > requirements.txt`
│
├── setup.py           <- makes project pip installable (pip install -e .) so src can be imported
├── src                <- Source code for use in this project.
│   ├── __init__.py    <- Makes src a Python modu
│   │
│   ├── data           <- Scripts to download or generate data
│   │   └── make_dataset.py
│   │
│   ├── features       <- Scripts to turn raw data into features for modeling
│   │   └── build_features.py
│   │
│   ├── models         <- Scripts to train models and then use trained models to make
│   │   │                 predictions
│   │   ├── predict_model.py
│   │   └── train_model.py
│   │
│   └── visualization  <- Scripts to create exploratory and results oriented visualizations
│       └── visualize.py
│
└── tox.ini            <- tox file with settings for running tox; see tox.testrun.org

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Project based on the cookiecutter data science project template. #cookiecutterdatascience

Contributing

Contributions are what make the open source community such an amazing place to be learn, inspire, and create. Any contributions you make are greatly appreciated.

License

Distributed under the MIT License. See LICENSE for more information.

Contact

Em@il : - gorskimichal27@gmail.com

Linkedin : https://www.linkedin.com/in/migorski/

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Created with ❤️

By Michal Gorski