文中的方法是EEG源成像（ESI）+ Morlet小波聯合時頻分析（JTFA）+卷積神經網絡（CNN）。原始數據已使用Matlab ToolkitBrainstorm處理。在ESI + JTFA過程處理之后，使用CNN對EEG數據進行分類。

EEG Motor Imagery Signals (Tasks) Classification 

via Convolutional Neural Networks (CNN)

代碼地址：

https://github.com/SuperBruceJia/EEG-Motor-Imagery-Classification-CNNs-TensorFlow

安裝使用

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

CNN網絡架構代碼：MI_Proposed_CNNs_Architecture