amnoise | |||
---|---|---|---|
trial_type | subject | auditory | |
102 | 110 |
1 rows × 2 columns
General | ||
---|---|---|
Filename(s) | sub-102_task-amnoise_ave.fif | |
MNE object type | Evoked | |
Measurement date | 2000-01-01 at 00:00:00 UTC | |
Participant | sub-102 | |
Experimenter | mne_anonymize | |
Acquisition | ||
Aggregation | average of 1 epochs | |
Condition | Grand average: auditory | |
Time range | -0.200 – 1.000 s | |
Baseline | -0.200 – 0.000 s | |
Sampling frequency | 200.00 Hz | |
Time points | 241 | |
Channels | ||
Magnetometers | ||
Gradiometers | ||
Head & sensor digitization | 167 points | |
Filters | ||
Highpass | 0.03 Hz | |
Lowpass | 40.00 Hz | |
Projections |
meg-ECG--0.500-0.500)-PCA-01 (on)
meg-ECG--0.500-0.500)-PCA-02 (on) |
"""Single-subject infant dataset for testing maxwell_filter with movecomp.
https://openneuro.org/datasets/ds004229
"""
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"
Platform Linux-5.15.0-1057-aws-x86_64-with-glibc2.35
Python 3.10.12 (main, Nov 20 2023, 15:14:05) [GCC 11.4.0]
Executable /home/circleci/python_env/bin/python3.10
CPU x86_64 (36 cores)
Memory 68.6 GB
Core
├☑ mne 1.8.0.dev79+gcaba81b9f (devel, latest release is 1.7.1)
├☑ numpy 2.0.0 (OpenBLAS 0.3.27 with 2 threads)
├☑ scipy 1.13.1
└☑ matplotlib 3.9.0 (backend=agg)
Numerical (optional)
├☑ sklearn 1.5.0
├☑ numba 0.60.0
├☑ nibabel 5.2.1
├☑ pandas 2.2.2
├☑ h5io 0.2.3
├☑ h5py 3.11.0
└☐ unavailable nilearn, dipy, openmeeg, cupy
Visualization (optional)
├☑ pyvista 0.43.10 (OpenGL 4.5 (Core Profile) Mesa 23.2.1-1ubuntu3.1~22.04.2 via llvmpipe (LLVM 15.0.7, 256 bits))
├☑ pyvistaqt 0.11.1
├☑ vtk 9.3.0
├☑ qtpy 2.4.1 (PyQt6=6.7.1)
└☐ unavailable ipympl, pyqtgraph, mne-qt-browser, ipywidgets, trame_client, trame_server, trame_vtk, trame_vuetify
Ecosystem (optional)
├☑ mne-bids 0.16.0.dev3+g0e3cbc66a
├☑ mne-bids-pipeline 1.9.0
├☑ edfio 0.4.2
├☑ pybv 0.7.5
└☐ unavailable mne-nirs, mne-features, mne-connectivity, mne-icalabel, neo, eeglabio, mffpy