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
Resources
Instructions
Install the dependencies
It is recommended to create a virtual environment with python version 3.7 and activate it before running the following:
pip install -r requirements.txtObtain the raw dataset
Download the raw dataset from the 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.
python preprocess_h5_smt.py $source $targetTraining 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
python train_within.py $datapath $outpathSubject-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 file.
To train the subject-independent model for all subjects, run
python train_base.py $datapath $outpath -fold $foldfor $fold ranging [0...53] inclusive.
This process is likely to take some time. We have provided some sample pre-trained models in the 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:
python train_adapt.py $datapath $modelpath $outpathThe $modelpath corresponds to the result folder of the 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:
python generate.py