Code for MUPS model introduced in "Ultra Efficient Transfer Learning with Meta Update for Cross Subject EEG Classification".

The model is implemented with Pytorch, we recommend python 3.5 and PyTorch 0.4.0 with Anaconda.

Create a new environment and install python packages in it:

   conda create --name mups python=3.5
   conda activate mups
   conda install pytorch=0.4.0
   conda install scipy scikit-learn

Install other dependencies:

   pip install tqdm tensorboardX

Clone the repository:

   git clone https://github.com/tiehangd/MUPS

Download BCI-IV 2a dataset from http://bnci-horizon-2020.eu/database/data-sets, Four class motor imagery (001-2014)

Place the 18 files inside ./data folder

Data preprocess, run from command line

   python ./dataloader/data_preprocessing.py

This produces data for our cross subject task, which is stored in ./data/cross_sub

1. Pretraining of feature extractor

   python pre_train.py
   

2. Meta adaptation

   python meta_adapt.py
   

Please cite our paper if it is helpful to your work:

@article{Duan2020,
  title={Ultra Efficient Transfer Learning with Meta Update for Cross Subject EEG Classification},
  author={Tiehang Duan and Mihir Chauhan and Mohammad Abuzar Shaikh and Sargur N. Srihari},
  journal={ArXiv},
  year={2020},
  volume={abs/2003.06113}
}

Implementation of MUPS model utilized code from the following repositories:

1) https://github.com/yaoyao-liu/meta-transfer-learning
2) https://github.com/aliasvishnu/EEGNet