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)
bids_root = "~/mne_data/ds004107"
deriv_root = "~/mne_data/derivatives/mne-bids-pipeline/ds004107"
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