MIND DATA.

M.P. Weisend, F.M. Hanlon, R. Montaño, S.P. Ahlfors, A.C. Leuthold, D. Pantazis, J.C. Mosher, A.P. Georgopoulos, M.S. Hämäläinen, C.J. Aine,, V. (2007). Paving the way for cross-site pooling of magnetoencephalography (MEG) data. International Congress Series, Volume 1300, Pages 615-618.

Demonstrated features

Feature	This example
MEG processing	✅
EEG processing	❌
Maxwell filter	❌
Frequency filter	✅
Artifact regression	❌
SSP	✅
ICA	❌
Evoked contrasts	❌
Time-by-time decoding	❌
Time-generalization decoding	❌
CSP decoding	❌
Time-frequency analysis	❌
BEM surface creation	❌
Template MRI	❌

Dataset source

This dataset was acquired from https://openneuro.org/datasets/ds004107

How to download this dataset

Run in your terminal:

Run in your terminal

openneuro-py download \
             --dataset=ds004107 \
             --include=sub-mind002/ses-01/meg/*coordsystem* \
             --include=sub-mind002/ses-01/meg/*auditory*

Configuration

Click to expand

Python

# This has auditory, median, indx, visual, rest, and emptyroom but let's just
# process the auditory (it's the smallest after rest)
study_name = "ds004107"
bids_root = f"~/mne_data/{study_name}"
deriv_root = f"~/mne_data/derivatives/mne-bids-pipeline/{study_name}"
subjects = ["mind002"]
sessions = ["01"]
conditions = ["left", "right"]  # there are also tone and noise
task = "auditory"
ch_types = ["meg"]
crop_runs = (0, 120)  # to speed up computations
spatial_filter = "ssp"
l_freq = 1.0
h_freq = 40.0