func_mining

Code for the paper "Mining within-trial oscillatory brain dynamics to address the variability of optimized spatial filters" in IEEE Trans. on Neural Systems & Rehabiliation Engineering (2019).

How to run the example notebook

The code was tested on Python 3.5.2, other version may work as well, but we recommend using a version >=3.5.2. Note that the following instructions are written for users using a default Python3 installation. Commands may differ for AnaConda users.

0. Set up a virtual environment (recommended)

It's always a good idea to set up a virtual environment for new projects. In order to create a virtual environment, change to the directory in which you cloned this repository and execute:

python3 -m venv name_of_your_venv_folder

Now activate your environment:

source name_of_your_venv_folder/bin/activate

Use deactivate to leave the virtual environment after you are done with this notebook / repository.

1. Download the provided data set

Run make data in the root folder of the repository.

Alternatively, download the provided exemplary features set of an exemplary subject available on zenodo. Unzip the data and place the "processed" folder under ./data of this repository.

2. Install requirements

pip install -r requirements.txt

3. Run the jupyter notebook

Make sure you are currently in the repository root and then:

jupyter notebook notebooks/Mining_functional_brain_signals.ipynb

The notebook should open automatically in your default web browser. If it does not do so, visit localhost:8888 in your favourite browser.

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
│   └── processed      <- Exemplary dataset of different spatial filters computed on data of one subject 
│    
├── notebooks          <- Example jupyter notebook for using our proposed method. 
│
├── requirements.txt   <- The requirements file for reproducing the analysis environment, e.g.
│                         generated with `pip install -r 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 module
│   │
│   ├── data           <- Import component data
│   │    
│   ├── features       <- Feature preprocessing for clustering    │   │  
│   │
│   ├── models         <- DBSCAN clustering
│   │     
│   └── visualization  <- Varioous tools to inspect / characterize the clustering results
│                        
│
└── .gitignore         <- list of files to ignore for git 

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