How to do cluster-based permutation test of MEG data in group-level

  • MNE version: 1.5.1
  • operating system: macOS Sonoma 14.3

Dear all,

I am now trying to do data analysis in Python-mne. But when I try to follow the turtorial “Spatiotemporal permutation F-test on full sensor data”, I faced an issue about group level analysis.

In my script, I save all my epochs’ data as list to achieve the group-level analysis. But when I try to concatenate the epochs with mne.concatenate_epochs function, the python report a bug as “ValueError: epochs[1].info[‘dev_head_t’] differs. The instances probably come from different runs, and are therefore associated with different head positions. Manually change info[‘dev_head_t’] to avoid this message but beware that this means the MEG sensors will not be properly spatially aligned. See mne.preprocessing.maxwell_filter to realign the runs to a common head position”.

Following the chatgpt suggestions, I set the dev_head_t to an identity matrix. But in the following analysis, I failed to get a signjficant result. Therefore, I am wondering whether my result is influenced by this setting because it seems that the position information is changed and is not accurate at all after doing this setting. But I don’t know how to analysis the group-level data without doing this. The tutorial only give the single data analysis, not in a group level.

Does anyone know is there any other template about group-level analysis? I am quite confused about this.

I will be really appreciated for your help.