saman-rahbar / eeg-motor-imagery-classification-cnns-tensorflow Goto Github PK

1. Python file: PhysioNet_MI_Dataset/MIND_Get_EDF.py

   --- download all the EEG Motor Movement/Imagery Dataset .edf files from here!

   (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
   

At the root of the project, you will see:

├── 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

If you find our work useful in your research, please consider citing in your publications. We provide a BibTeX entry below.

@article{hou2019novel,  
    year = 2020,  
    month = {feb},  
    publisher = {{IOP} Publishing},  
    volume = {17},  
    number = {1},  
    pages = {016048},  
    author = {Yimin Hou and Lu Zhou and Shuyue Jia and Xiangmin Lun},  
    title = {A novel approach of decoding {EEG} four-class motor imagery tasks via scout {ESI} and {CNN}},  
    journal = {Journal of Neural Engineering}  
}  

We are very grateful to Prof. Yimin Hou due to his friendly guidance, and the research paper would not have happened without him.