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TensorFlow處理運動想象分類任務(wù)示例分析,針對這個問題,這篇文章詳細介紹了相對應的分析和解答,希望可以幫助更多想解決這個問題的小伙伴找到更簡單易行的方法。
文中的方法是EEG源成像(ESI)+ Morlet小波聯(lián)合時頻分析(JTFA)+卷積神經(jīng)網(wǎng)絡(luò)(CNN)。原始數(shù)據(jù)已使用Matlab ToolkitBrainstorm處理。在ESI + JTFA過程處理之后,使用CNN對EEG數(shù)據(jù)進行分類。
代碼地址:
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
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
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
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(wǎng)絡(luò)架構(gòu)代碼:MI_Proposed_CNNs_Architecture
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