Single-subject infant dataset for testing maxwell_filter with movecomp.¶
https://openneuro.org/datasets/ds004229
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/ds004229
How to download this dataset
Run in your terminal:
Run in your terminal
openneuro-py download \
--dataset=ds004229 \
--include=sub-102 \
--include=sub-emptyroom/ses-20000101
Configuration¶
Click to expand
Python
import mne
import numpy as np
bids_root = "~/mne_data/ds004229"
deriv_root = "~/mne_data/derivatives/mne-bids-pipeline/ds004229"
task = "amnoise"
crop_runs = (300.0, 600.0) # 5 minutes from the middle of the recording for speed
find_flat_channels_meg = True
find_noisy_channels_meg = True
use_maxwell_filter = True
mf_destination = mne.transforms.translation( # rotate backward and move up
z=0.055,
) @ mne.transforms.rotation(x=np.deg2rad(-15))
mf_mc = True
mf_st_duration = 10
mf_int_order = 6 # lower for smaller heads
mf_mc_t_step_min = 0.5 # just for speed!
mf_mc_t_window = 0.2 # cleaner cHPI filtering on this dataset
mf_filter_chpi = False # for speed, not needed as we low-pass anyway
mf_mc_rotation_velocity_limit = 30.0 # deg/s for annotations
mf_mc_translation_velocity_limit = 20e-3 # m/s
mf_esss = 8
mf_esss_reject = {"grad": 10000e-13, "mag": 40000e-15}
ch_types = ["meg"]
l_freq = None
h_freq = 40.0
# SSP and peak-to-peak rejection
spatial_filter = "ssp"
n_proj_eog = dict(n_mag=0, n_grad=0)
n_proj_ecg = dict(n_mag=2, n_grad=2)
ssp_ecg_channel = "MEG0113" # ECG channel is not hooked up in this dataset
reject = ssp_reject_ecg = {"grad": 2000e-13, "mag": 5000e-15}
# Epochs
epochs_tmin = -0.2
epochs_tmax = 1
epochs_decim = 6 # 1200->200 Hz
baseline = (None, 0)
report_add_epochs_image_kwargs = {
"grad": {"vmin": 0, "vmax": 1e13 * reject["grad"]}, # fT/cm
"mag": {"vmin": 0, "vmax": 1e15 * reject["mag"]}, # fT
}
# Conditions / events to consider when epoching
conditions = ["auditory"]
# Decoding
decode = False
# Noise estimation
noise_cov = "emptyroom"