In the group-level GLM tutorial, raw_haemo is returned after running each individual participant separately.
df_roi = pd.DataFrame() # To store region of interest results
df_cha = pd.DataFrame() # To store channel level results
df_con = pd.DataFrame() # To store channel level contrast results
for sub in subjects: # Loop from first to fifth subject
# Create path to file based on experiment info
bids_path = dataset.update(subject=sub)
# Analyse data and return both ROI and channel results
raw_haemo, roi, channel, con = individual_analysis(bids_path, sub)
# Append individual results to all participants
df_roi = pd.concat([df_roi, roi], ignore_index=True)
df_cha = pd.concat([df_cha, channel], ignore_index=True)
df_con = pd.concat([df_con, con], ignore_index=True)
This is not stored anywhere, nor aggregated across participants. Yet, later in the tutorial, raw_haemo is used in group topographic visualizations and contrast results. As the code is written, I believe that the last participant's raw data will be used, rather than some form of group average. Is this expected behavior? Thanks.
In the group-level GLM tutorial,
raw_haemois returned after running each individual participant separately.This is not stored anywhere, nor aggregated across participants. Yet, later in the tutorial,
raw_haemois used in group topographic visualizations and contrast results. As the code is written, I believe that the last participant's raw data will be used, rather than some form of group average. Is this expected behavior? Thanks.