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.
@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",
}
| 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
It is recommended to create a virtual environment with python version 3.7 and activate it before running the following:
pip install -r requirements.txtDownload 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.
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 $targetusage: 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 $outpathusage: 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.
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.
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