Welcome to BCI Lib!
BCI Lib
Basic information
Implementation code for BCI models. The source code presented on the github.
All methods were implemented based on pytorch for simple parallelization by using cuda.
All information about this project can be found in the documentation.
Requirements and Installation
A simple instruction of installation using pip is provided near the source code.
More information about installation can be found in documentation installation page.
Example of use
A simple examples of module usage can be found in documentation example page.
Installation
Requirements
Python >= 3.8
pip >= 22.0
Installing by using PyPi
Install
python3 -m pip install bci
Uninstall
python3 -m pip uninstall bci
Installing from GitHub source
Install
git clone https://github.com/intsystems/bci.git
cd bci
python3 -m pip install ./src/
Uninstall
python3 -m pip uninstall bci
Example
Requirements
It is recommended make virtualenv and install all next packages in this virtualenv.
bci==0.0.1
Include packages.
TODO
Preparing the dataset
TODO
Regularizers
The bci.base_models contains classes:
- class bci.base_models.BaseModel[source]
Base class for the BCI models.
- class bci.base_models.IdentityModel[source]
A model which defines identity mapping. Mathematically define model \(\textbf{f}(\textbf{x}) = \textbf{x}\).
Warning
It’s just an example of BCI model, and cannot be used in real cases.
Example:
>>> _ = torch.random.manual_seed(42) # Set random seed for repeatability >>> >>> model = IdentityModel() >>> X = torch.randn(2, 1) # Generate random tensor >>> predict = model(X) tensor([[0.3367], [0.1288]])