MUPS

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

Setting up environment:

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

Create a new environment and install PyTorch on it:
```
conda create --name mups python=3.5
conda activate mups
conda install pytorch=0.4.0
```
Install necessary python packages:
```
pip install tqdm tensorboardX
```
Clone the repository:
```
git clone https://github.com/tiehangd/MUPS
```
Dataset preparation:

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
Running the model:
1. Pretraining of feature extractor
```
python pre_train.py
```
2. Meta adaptation
```
python meta_adapt.py
```
Acknowledgements:

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

gzoumpourlis/MUPS