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
.
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
process_empty_room = 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