Faces dataset

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/ds000117

How to download this dataset

Run in your terminal:

Run in your terminal

openneuro-py download \
             --dataset=ds000117 \
             --include=sub-01/ses-meg/meg/sub-01_ses-meg_task-facerecognition_run-01_* \
             --include=sub-01/ses-meg/meg/sub-01_ses-meg_task-facerecognition_run-02_* \
             --include=sub-01/ses-meg/meg/sub-01_ses-meg_headshape.pos \
             --include=sub-01/ses-meg/*.tsv \
             --include=sub-01/ses-meg/*.json \
             --include=sub-emptyroom/ses-20090409 \
             --include=derivatives/meg_derivatives/ct_sparse.fif \
             --include=derivatives/meg_derivatives/sss_cal.dat

Configuration

Click to expand

Python

.

study_name = "ds000117"
bids_root = "~/mne_data/ds000117"
deriv_root = "~/mne_data/derivatives/mne-bids-pipeline/ds000117"

task = "facerecognition"
ch_types = ["meg"]
runs = ["01", "02"]
sessions = ["meg"]
subjects = ["01"]

raw_resample_sfreq = 125.0
crop_runs = (0, 300)  # Reduce memory usage on CI system

find_flat_channels_meg = True
find_noisy_channels_meg = True
use_maxwell_filter = True

mf_reference_run = "02"
mf_cal_fname = bids_root + "/derivatives/meg_derivatives/sss_cal.dat"
mf_ctc_fname = bids_root + "/derivatives/meg_derivatives/ct_sparse.fif"

reject = {"grad": 4000e-13, "mag": 4e-12}
conditions = ["Famous", "Unfamiliar", "Scrambled"]
contrasts = [
    ("Famous", "Scrambled"),
    ("Unfamiliar", "Scrambled"),
    ("Famous", "Unfamiliar"),
]

decode = True
decoding_time_generalization = True

run_source_estimation = False