ds003392: hMT+ Localizer (OTP + Maxwell filter).¶
See OpenNeuro for more information. This config tests implementation of oversampled temporal projection followed by maxwell filtering and subsequent preprocessing steps.
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 | ❌ |
| Dask | ❌ |
Dataset source¶
This dataset was acquired from https://openneuro.org/datasets/ds003392
How to download this dataset
Run in your terminal:
Run in your terminal
openneuro-py download \
--dataset=ds003392 \
--include=sub-01 \
--include=sub-emptyroom/ses-19111211
Configuration¶
Click to expand
Python
from mne.transforms import translation
bids_root = "~/mne_data/ds003392"
deriv_root = "~/mne_data/derivatives/mne-bids-pipeline/ds003392_otp_mxw"
ignore_warnings = [
"Internal Active Shielding data", # until MNE-BIDS releases a fix for ERM finding
]
subjects = ["01"]
task = "localizer"
# use oversampled temporal projection to clean sensor noise
use_otp_denoising = True
otp_duration = 10.0
# for OTP testing purposes, set to True
find_flat_channels_meg = True
find_noisy_channels_meg = True
use_maxwell_filter = True
mf_extra_kws = {"bad_condition": "warning"}
ch_types = ["meg"]
mf_esss = 1
# translation args should be x, y, z as scalar (int or float)
mf_destination = translation(z=0.04)
mf_cal_missing = "warn"
mf_ctc_missing = "warn"
l_freq = 1.0
h_freq = 40.0
raw_resample_sfreq = 250
crop_runs = (0, 20)
# Artifact correction
spatial_filter = "ica"
process_raw_clean = False
ica_algorithm = "picard-extended_infomax"
ica_max_iterations = 1000
ica_l_freq = 1.0
ica_n_components = 0.99
# Epochs
epochs_tmin = -0.2
epochs_tmax = 1.0
baseline = (None, 0)
# Conditions / events to consider when epoching
conditions = ["coherent", "incoherent"]