Note: The following instruction assumes that you're working on Shin's data

1.Import in data

• Modify the nirs_data_folder variable in shin_import.m file to your data location
• Run the shin_import.m script and you should have a protocol named "shin_workload" in your Brainstorm GUI

2. Selecting input data

• Each participant will have a single .nirs datafile
• Select your desire input file and drop them into the Files to process box below https://github.com/NK2306/Capstone_490/blob/main/images/Data_selecting.PNG
• Select RUN on the left of the Files to process box, the process GUI window should appear https://github.com/NK2306/Capstone_490/blob/main/images/Select_process.PNG
• Select the NIRS data preprocess, the following GUI should appear https://github.com/NK2306/Capstone_490/blob/main/images/NIRS_process.PNG

3. Selecting process options

1. Motion Correction checkbox

• Check box to apply motion correction process to raw NIRS data
• See Motion Correction for more details

2. NIRS preprocessing

• Number of participants: Enter the number of participants that you want to apply the NIRS preprocessing
• Number of trials for each n-back task: Enter the number of trials for each n-back task for your study
• Low-pass filter checkbox: Check box to apply low-pass filter to the input NIRS data
• Bad trial removal: Check box to apply bad trial removal process using coefficient variation. Set a couple options below to set the threshold for coefficient variation and percentage to disqualify a trial
• Generate average files: Check box to generate an average file for each n-back task for the participant

• Select RUN after setting all the desire options and wait for the process to finish running

4. Post run results:

• The process will generate a folder called NIRS_Data located in the {home}\.brainstorm folder that will contain all the files in .csv format
• This folder is the input of Machine Learning process