Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/zhangks98/eeg-adapt
Source Code for "Adaptive Transfer Learning with Deep CNN for EEG Motor Imagery Classification".
https://github.com/zhangks98/eeg-adapt
cnn deep-learning eeg motor-imagery
Last synced: 3 months ago
JSON representation
Source Code for "Adaptive Transfer Learning with Deep CNN for EEG Motor Imagery Classification".
- Host: GitHub
- URL: https://github.com/zhangks98/eeg-adapt
- Owner: zhangks98
- Created: 2020-03-15T07:58:00.000Z (almost 5 years ago)
- Default Branch: master
- Last Pushed: 2023-01-11T16:05:21.000Z (about 2 years ago)
- Last Synced: 2023-03-05T12:10:52.825Z (almost 2 years ago)
- Topics: cnn, deep-learning, eeg, motor-imagery
- Language: Python
- Homepage: https://doi.org/10.1016/j.neunet.2020.12.013
- Size: 28.6 MB
- Stars: 48
- Watchers: 5
- Forks: 28
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
Codes for adaptation of a subject-independent deep convolutional neural network (CNN) based electroencephalography (EEG)-BCI system for decoding hand motor imagery (MI). Five schemes are presented, each of which fine-tunes an extensively trained, pre-trained model and adapt it to enhance the evaluation performance on a target subject.
## Citation
```
@article{ZHANG20211,
title = "Adaptive transfer learning for EEG motor imagery classification with deep Convolutional Neural Network",
journal = "Neural Networks",
volume = "136",
pages = "1 - 10",
year = "2021",
issn = "0893-6080",
doi = "https://doi.org/10.1016/j.neunet.2020.12.013",
url = "http://www.sciencedirect.com/science/article/pii/S0893608020304305",
author = "Kaishuo Zhang and Neethu Robinson and Seong-Whan Lee and Cuntai Guan",
}
```
## Summary of Results
| Methodology | Mean (SD) | Median | Range (Max-Min) |
|-|-|-|-|
| Subject-Specific | 63.54 (14.25) | 60.50 | 57.00 (100.00-43.00) |
| Subject-Independent | 84.19 (9.98) | 84.50 | 47.50 (99.50-52.00) |
| Subject-Adaptive
(Scheme 4, 80%) | 86.89 (11.41) | 88.50 | 44.00 (100.00-56.00) |
A detailed subject-level result can be found in [result_table.pdf](result_table.pdf)
## Resources
- Raw Dataset: [Link](http://gigadb.org/dataset/100542)
- Sample pre-trained subject-independent model: [Link](https://github.com/zhangks98/eeg-adapt/tree/master/pretrained_models)
## Instructions
### Install the dependencies
It is recommended to create a virtual environment with python version 3.7 and activate it before running the following:
```sh
pip install -r requirements.txt
```
### Obtain the raw dataset
Download the raw dataset from the [resources](#resources) above, and save them to the same folder. To conserve space, you may only download files that ends with `EEG_MI.mat`.
### Pre-process the raw dataset
The following command will read the raw dataset from the `$source` folder, and output the pre-processed data `KU_mi_smt.h5` into the `$target` folder.
```sh
python preprocess_h5_smt.py $source $target
```
### Training and evaluation
#### Subject-specific classification
```
usage: train_within.py [-h] [-gpu GPU] [-start START] [-end END] [-subj SUBJ [SUBJ ...]] datapath outpath
Subject-specific classification with KU Data
positional arguments:
datapath Path to the h5 data file
outpath Path to the result folder
optional arguments:
-h, --help show this help message and exit
-gpu GPU The gpu device index to use
-start START Start of the subject index
-end END End of the subject index (not inclusive)
-subj SUBJ [SUBJ ...]
Explicitly set the subject number. This will override the start and end argument
```
To train the subject-specific model for all subjects, run
```sh
python train_within.py $datapath $outpath
```
#### Subject-independent classification
```
usage: train_base.py [-h] -fold FOLD [-gpu GPU] datapath outpath
Subject independent classification with KU Data
positional arguments:
datapath Path to the h5 data file
outpath Path to the result folder
optional arguments:
-h, --help show this help message and exit
-fold FOLD k-fold index, starts with 0
-gpu GPU The gpu device to use
```
The `$fold` index has a one-to-one mapping to the subject index, as we have shuffled the subjects in a pre-defined order (using random seed 20200205). This is listed in [subj-to-fold.csv](subj-to-fold.csv) file.
To train the subject-independent model for all subjects, run
```sh
python train_base.py $datapath $outpath -fold $fold
```
for `$fold` ranging [0...53] inclusive.
This process is likely to take some time. We have provided some sample pre-trained models in the [resources](#resources) above.
#### Subject-adaptive classification
```
usage: train_adapt.py [-h] [-scheme SCHEME] [-trfrate TRFRATE] [-lr LR] [-gpu GPU] datapath modelpath outpath
Subject adaptative classification with KU Data
positional arguments:
datapath Path to the h5 data file
modelpath Path to the base model folder
outpath Path to the result folder
optional arguments:
-h, --help show this help message and exit
-scheme SCHEME Adaptation scheme
-trfrate TRFRATE The percentage of data for adaptation
-lr LR Learning rate
-gpu GPU The gpu device to use
```
As an example, to train the subject-adaptive model for all subjects using the default configuration (scheme 4, adaptation rate 100%, learning rate 0.0005), run:
```sh
python train_adapt.py $datapath $modelpath $outpath
```
The `$modelpath` corresponds to the result folder of the [subject-independent classification](#subject-independent-classification), or the path to the pre-trained model.
### Training on multiple GPUs
To speed up training, you can make use of multiple GPUs. In the `tasks_*` folder, we provide a sample script for training on 8 GPUs. To generate these scripts, run:
```sh
python generate.py
```