https://github.com/superbrucejia/eeg-motor-imagery-classification-cnns-tensorflow
EEG Motor Imagery Tasks Classification (by Channels) via Convolutional Neural Networks (CNNs) based on TensorFlow
https://github.com/superbrucejia/eeg-motor-imagery-classification-cnns-tensorflow
brain-com brain-computer-interface cnns convolutional-neural-networks eeg eeg-analysis eeg-classification eeg-data eeg-signals esi matlab motor-imagery motor-imagery-classification motor-imagery-tasks motor-imagery-training python tensorflow tensorflow-experiments tensorflow-models
Last synced: 13 days ago
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EEG Motor Imagery Tasks Classification (by Channels) via Convolutional Neural Networks (CNNs) based on TensorFlow
- Host: GitHub
- URL: https://github.com/superbrucejia/eeg-motor-imagery-classification-cnns-tensorflow
- Owner: SuperBruceJia
- Created: 2019-06-04T11:09:00.000Z (almost 6 years ago)
- Default Branch: master
- Last Pushed: 2024-05-20T00:58:46.000Z (11 months ago)
- Last Synced: 2025-04-09T18:18:20.065Z (13 days ago)
- Topics: brain-com, brain-computer-interface, cnns, convolutional-neural-networks, eeg, eeg-analysis, eeg-classification, eeg-data, eeg-signals, esi, matlab, motor-imagery, motor-imagery-classification, motor-imagery-tasks, motor-imagery-training, python, tensorflow, tensorflow-experiments, tensorflow-models
- Language: Python
- Homepage: https://iopscience.iop.org/article/10.1088/1741-2552/ab4af6/meta
- Size: 56.6 KB
- Stars: 217
- Watchers: 4
- Forks: 48
- Open Issues: 3
-
Metadata Files:
- Readme: README.md
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README
## EEG Motor Imagery Signals (Tasks) Classification via Convolutional Neural Networks (CNN)
**Author**: Shuyue Jia and Lu Zhou, School of Automation Engineering, Northeast Electric Power University, Jilin, China.
**Date**: December of 2018
### Download Paper
[A Novel Approach of Decoding EEG Four-class Motor Imagery Tasks via Scout ESI and CNN](https://iopscience.iop.org/article/10.1088/1741-2552/ab4af6/meta)
NOTICE: The method in our paper is EEG source imaging (ESI) + Morlet wavelet joint time-frequency analysis (JTFA) + Convolutional Neural Networks (CNNs). The raw data has been processed using the Matlab Toolkit [Brainstorm](https://neuroimage.usc.edu/brainstorm/). My job is using CNNs to classify the EEG data after the ESI + JTFA process. The Dataset (.mat Files) preprocessed via the ESI + JTFA process can be found via the [Shared Google Drive](https://drive.google.com/drive/folders/1qCzC9a4cKsjXriba-UECKLEGCJbcwc3e?usp=sharing). The corresponding preprocessed Excel files, trained checkpoints, and evaluation results can be downloaded from the [Shared Google Drive](https://drive.google.com/drive/folders/1-OIiLeVj0KMpKqXe6FJe6YntRuAIVUIF?usp=sharing).
Meanwhile, the codes in this repository are based on the raw EEG data without the ESI and JTFA process, and can also achieve a good result. The main CNNs Tensorflow framework codes in the "MI_Proposed_CNNs_Architecture.py" are the same for both of the works.
---
**Overall Framework**:
**Proposed CNNs Architecture**:
---
### Installation and Usage
1. Python file: PhysioNet_MI_Dataset/MIND_Get_EDF.py
--- download all the EEG Motor Movement/Imagery Dataset .edf files from [here](https://archive.physionet.org/pn4/eegmmidb/)!
```
(Under Any Python Environment) $ python MIND_Get_EDF.py
```
2. Python file: Read_Raw_Data_Save_Into_Matlab_Files.py
--- Read the edf Raw data of different channels and save them into matlab .m files
--- At this stage, the Python file must be processed under a Python 2 environment (I recommend to use Python 2.7 version).
```
(Under Python 2.7 Environment) $ python Read_Raw_Data_Save_Into_Matlab_Files.py
```
3. Matlab file: Saved_Matlab_Data/Preprocessing_Raw_Data.m
--- Pre-process the dataset (Data Normalization mainly) and save matlab .m files into Excel .xlsx Files
4. Python file: MI_Proposed_CNNs_Architecture.py
--- the proposed CNNs architecture
--- based on TensorFlow 1.12.0 with CUDA 9.0 or TensorFlow 1.13.1 with CUDA 10.0
--- The trained results are saved in the Tensorboard
--- Open the Tensorboard and save the results into Excel .csv files
--- Draw the graphs using Matlab or Origin
```
(Under Python 3.6 Environment) $ python MI_Proposed_CNNs_Architecture.py
```
### Structure of the code
At the root of the project, you will see:
```text
├── PhysioNet_MI_Dataset
| └── MIND_Get_EDF.py
├── Read_Raw_Data_Save_Into_Matlab_Files.py
├── Saved_Matlab_Data
| └── Preprocessing_Raw_Data.m
├── MI_Proposed_CNNs_Architecture.py
├── electrode_positions.txt
```
### Citation
If you find our work useful in your research, please consider citing it in your publications. We provide a BibTeX entry below.
```bibtex
@article{hou2020novel,
title = {A Novel Approach of Decoding EEG Four-class Motor Imagery Tasks via Scout ESI and CNN},
author = {Hou, Yimin and Zhou, Lu and Jia, Shuyue and Lun, Xiangmin},
journal = {Journal of Neural Engineering},
volume = {17},
number = {1},
pages = {016048},
year = {Feb. 2020},
publisher = {IOP Publishing}
}
```
### Acknowledgment
We are very grateful to Prof. Yimin Hou due to his friendly guidance, and the research paper would not have happened without him.
