Copyright (C) 2020 ETH Zurich, Switzerland. SPDX-License-Identifier: Apache-2.0. See LICENSE file for details.
Authors: Thorir Mar Ingolfsson, Michael Hersche, Xiaying Wang, Nobuaki Kobayashi, Lukas Cavigelli, Luca Benini
# EEG-TCNet
This project provides the experimental environment used to produce the results reported in the paper *EEG-TCNet: An Accurate Temporal Convolutional Network for Embedded Motor-Imagery Brain-Machine Interfaces* available on [arXiv](https://arxiv.org/abs/2006.00622). If you find this work useful in your research, please cite
```
@misc{ingolfsson2020eegtcnet,
title={EEG-TCNet: An Accurate Temporal Convolutional Network for Embedded Motor-Imagery Brain-Machine Interfaces},
author={Thorir Mar Ingolfsson and Michael Hersche and Xiaying Wang and Nobuaki Kobayashi and Lukas Cavigelli and Luca Benini},
year={2020},
eprint={2006.00622},
archivePrefix={arXiv},
primaryClass={eess.SP}
}
```
## Getting started
### Prerequisites
* We developed and used the code behind EEG-TCNet on [Ubuntu 18.04.3 LTS (Bionic Beaver) (64bit)](http://old-releases.ubuntu.com/releases/18.04.3/).
* The code behind EEG-TCNet is based on Python3, and [Anaconda3](https://www.anaconda.com/distribution/) is required.
* We used [NVidia GTX1080 Ti GPUs](https://developer.nvidia.com/cuda-gpus) to accelerate the training of our models (driver version [396.44](https://www.nvidia.com/Download/driverResults.aspx/136950/en-us)). In this case, CUDA and the cuDNN library are needed (we used [CUDA 10.1](https://developer.nvidia.com/cuda-toolkit-archive)).
Also the dataset 2a of the BCI Competition IV needs to be downloaded and put into the `/data` folder. It is available on [here](http://bnci-horizon-2020.eu/database/data-sets)
### Installing
Navigate to EEG-TCNet's main folder and create the environment using Anaconda: (We have two environments (one for GPU and one for CPU)) for GPU do:
```
$ conda env create -f EEG-TCNet-GPU.yml -n EEG-TCNet
```
For CPU do
```
$ conda env create -f EEG-TCNet.yml -n EEG-TCNet
```
## Usage
We provide the models under `/models/EEG-TCNet` inside there we have 9 subdirectories `/S1` to `/S9` each representing each subject. Inside each subdirectory there are 6 files. `model.h5` is the saved keras model of variable EEG-TCNet and `model_fixed.h5` is the saved keras model of fixed EEG-TCNet. Then there are two pipeline files in each subdirectory which vary depending on if data normalization was used or not. Please refer to `Accuracy_and_kappa_scores.ipynb` in the main directory to see how these pipelines are produces. There you also find the accuracy score and kappa score verification of EEG-TCNet.
Under `/utils` you find the data loading and model making files. Then also a small sample of how to train is given with `sample_train.py`, please note that because of the stochastic nature of training with GPUs it's very hard to fix every random variable in the backend. Therefore to reproduce the same or similar models one might need to train a couple of times in order to get the same highly accurate models we present.
### License and Attribution
Please refer to the LICENSE file for the licensing of our code.
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eeg-tcnet-master.rar (56个子文件)
eeg-tcnet-master
EEG-TCNet-GPU.yml 3KB
EEG-TCNet.yml 3KB
data
LICENSE 9KB
sample_train.py 3KB
说明.txt 254B
utils
models.py 5KB
data_loading.py 4KB
__pycache__
data_loading.cpython-36.pyc 3KB
models.cpython-36.pyc 3KB
.idea
eeg-tcnet-master.iml 498B
workspace.xml 4KB
misc.xml 199B
inspectionProfiles
profiles_settings.xml 174B
modules.xml 291B
.gitignore 50B
Accuracy_and_kappa_scores.ipynb 18KB
models
EEG-TCNet
S7
pipeline_fixed.h5 633KB
model.h5 228KB
pipeline.h5 648KB
model_fixed.h5 182KB
S4
pipeline_fixed.h5 633KB
model.h5 305KB
pipeline.h5 671KB
model_fixed.h5 182KB
S9
pipeline_fixed.h5 633KB
model.h5 287KB
pipeline.h5 663KB
model_fixed.h5 182KB
S5
pipeline_fixed.h5 633KB
model.h5 431KB
pipeline.h5 711KB
model_fixed.h5 182KB
S1
pipeline_fixed.h5 633KB
model.h5 235KB
pipeline.h5 647KB
model_fixed.h5 182KB
LICENSE 18KB
S8
pipeline_fixed.h5 633KB
model.h5 355KB
pipeline.h5 688KB
model_fixed.h5 182KB
S3
pipeline_fixed.h5 633KB
model.h5 200KB
pipeline.h5 639KB
model_fixed.h5 182KB
S2
pipeline_fixed.h5 633KB
model.h5 212KB
pipeline.h5 57KB
model_fixed.h5 182KB
README.md 1KB
S6
pipeline_fixed.h5 633KB
model.h5 305KB
pipeline.h5 671KB
model_fixed.h5 182KB
.gitignore 55B
README.md 3KB
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