This repository describes a GUI developed by Dr. Jin Jing ("JJ"), PhD that enables rapid labeling of IIIC patterns. By following these instructions you can set up the GUI to annotate your own data.
Files and data can be downloaded from this GitHub repository, or here
Requirements: MATLAB, EEGLAB (https://sccn.ucsd.edu/eeglab/index.php), and Python (Anaconda)
Input data: Raw EDF files inside .\Data\EDF; scalp monopolar/C2 EEG that contains a full set of 19 channels + 1 EKG (optional), with channels named as follows:
Fp1 F3 C3 P3 F7 T3 T5 O1 Fz Cz Pz Fp2 F4 C4 P4 F8 T4 T6 O2 (EKG)
Step1: Read EDF to MAT using EEGLAB toolbox for MATLAB. Run script step1_readEDF2MAT.m, which converts EDF format to MAT format in .\Data\MAT\ that contains the following variables:
- data: EEG array
- channels: list of channel names in data
- Fs: the sampling rate of data
- startTime: the start time vector of data
Step2: Preprocess MAT to select/rearrange channels, resample to 200Hz , and denoise with 0.5 40Hz band-pass and 5Hz band-stop centered at the power-line frequency (US: 60Hz, UK: 50Hz). Output files are saved in .\Data\processed\
Step3: run SPaRCNet (Python backend)
==Configure Python==
Install anaconda3 and open a terminal
$ conda create -n iiic python=3.6
$ activate iiic
$ conda install -c conda-forge hdf5storage
$ pip install mne
$ pip install torch==1.5.0+cpu torchvision==0.6.0+cpu -f https://download.pytorch.org/whl/torch_stable.html
Run MATLAB wrapper step3_runSPaRCNat.m
CSV score table will be export to .\Data\iiic\
Each row is the probabilities for 6 classes: Other, Seizure, LPD, GPD, LRDA, and GRDA
Starting from the 1^st^ 10sec EEG segment and moving at 2sec step in time
Eg. row #1: scores for 0 10sec, row #2: scores for 2 12sec, ...
Step4: Run step4_readCSV.m to read CSV to MAT to make sure every 2sec segment has scores. The output files are saved in .\Data\iiic\model_prediction.
Step5: Run step5_computeSpectrograms to get regional average spectrograms in .\Data\Spectrograms, which contains the following variables:
- Sdata: 4 regional average spectrograms
- stimes: time coordinates
- sfreqs: frequency coordinates
- params: spectrogram parameters
Step6: Compute embedding map (PaCMAP) and wrap all inputs into one task folder for each patient
Configure PaCMAP (Python library: https://github.com/YingfanWang/PaCMAP)
Run step6_prepare4GUI.m
Step7: Run GUI CMGUI_Sequential_BoWspreading_v3.m inside the task folder .\Task\subject01\
input rater initials to store scores.
click Start to continue.
Enter Phase #I: label K=50 clusters in a row.
Enter Phase #2: sequential inspect all labels.
Press Done button to seal and export the labels.
Please refer to slides for detailed instructions.