| funloc | ||||||
|---|---|---|---|---|---|---|
| trial_type | subject | auditory/deviant | auditory/standard | visual/deviant | visual/standard | |
| 01 | 10 | 60 | 10 | 60 | ||
| 02 | 10 | 60 | 10 | 60 | ||
2 rows × 5 columns
| General | ||
|---|---|---|
| Filename(s) | sub-01_task-funloc_ave.fif | |
| MNE object type | Evoked | |
| Measurement date | 2012-09-11 at 22:41:49 UTC | |
| Participant | sub-01 | |
| Experimenter | mne_anonymize | |
| Acquisition | ||
| Aggregation | average of 2 epochs | |
| Condition | Grand average: auditory/standard | |
| Time range | -0.200 – 0.500 s | |
| Baseline | -0.200 – 0.000 s | |
| Sampling frequency | 200.00 Hz | |
| Time points | 141 | |
| Channels | ||
| Magnetometers | ||
| Gradiometers | ||
| EEG | ||
| Head & sensor digitization | 197 points | |
| Filters | ||
| Highpass | 0.03 Hz | |
| Lowpass | 50.00 Hz | |
| Projections |
Average EEG reference (on)
meg-ECG--0.500-0.500)-PCA-01 (on) meg-EOG--0.500-0.500)-PCA-01 (on) eeg-EOG--0.500-0.500)-PCA-01 (on) eeg-EOG--0.500-0.500)-PCA-02 (on) |
|
| General | ||
|---|---|---|
| Filename(s) | sub-01_task-funloc_ave.fif | |
| MNE object type | Evoked | |
| Measurement date | 2012-09-11 at 22:41:49 UTC | |
| Participant | sub-01 | |
| Experimenter | mne_anonymize | |
| Acquisition | ||
| Aggregation | average of 2 epochs | |
| Condition | Grand average: visual/standard | |
| Time range | -0.200 – 0.500 s | |
| Baseline | -0.200 – 0.000 s | |
| Sampling frequency | 200.00 Hz | |
| Time points | 141 | |
| Channels | ||
| Magnetometers | ||
| Gradiometers | ||
| EEG | ||
| Head & sensor digitization | 197 points | |
| Filters | ||
| Highpass | 0.03 Hz | |
| Lowpass | 50.00 Hz | |
| Projections |
Average EEG reference (on)
meg-ECG--0.500-0.500)-PCA-01 (on) meg-EOG--0.500-0.500)-PCA-01 (on) eeg-EOG--0.500-0.500)-PCA-01 (on) eeg-EOG--0.500-0.500)-PCA-02 (on) |
|
"""Funloc data."""
from pathlib import Path
data_root = Path("~/mne_data").expanduser().resolve()
bids_root = data_root / "MNE-funloc-data"
deriv_root = data_root / "derivatives" / "mne-bids-pipeline" / "MNE-funloc-data"
subjects_dir = bids_root / "derivatives" / "freesurfer" / "subjects"
task = "funloc"
ch_types = ["meg", "eeg"]
data_type = "meg"
# filter
l_freq = None
h_freq = 50.0
# maxfilter
use_maxwell_filter: bool = True
crop_runs = (40, 190)
mf_st_duration = 60.0
# SSP
spatial_filter = "ssp"
ssp_ecg_channel = {"sub-01": "MEG0111", "sub-02": "MEG0141"}
n_proj_eog = dict(n_mag=1, n_grad=1, n_eeg=2)
n_proj_ecg = dict(n_mag=1, n_grad=1, n_eeg=0)
# Epochs
epochs_tmin = -0.2
epochs_tmax = 0.5
epochs_decim = 5 # 1000 -> 200 Hz
baseline = (None, 0)
conditions = [
"auditory/standard",
# "auditory/deviant",
"visual/standard",
# "visual/deviant",
]
decode = False
decoding_time_generalization = False
# contrasts
# contrasts = [("auditory", "visual")]
Platform Linux-5.15.0-1077-aws-x86_64-with-glibc2.35
Python 3.12.4 (main, Jun 8 2024, 23:40:19) [GCC 11.4.0]
Executable /home/circleci/.pyenv/versions/3.12.4/bin/python3.12
CPU Intel(R) Xeon(R) Platinum 8124M CPU @ 3.00GHz (36 cores)
Memory 68.6 GiB
Core
├☑ mne 1.10.0.dev67+gbe27cf8dd (devel, latest release is 1.9.0)
├☑ numpy 2.1.3 (OpenBLAS 0.3.27 with 2 threads)
├☑ scipy 1.15.2
└☑ matplotlib 3.10.1 (backend=agg)
Numerical (optional)
├☑ sklearn 1.6.1
├☑ numba 0.61.0
├☑ nibabel 5.3.2
├☑ pandas 2.2.3
├☑ h5io 0.2.4
├☑ h5py 3.13.0
└☐ unavailable nilearn, dipy, openmeeg, cupy
Visualization (optional)
├☑ pyvista 0.44.2 (OpenGL 4.5 (Core Profile) Mesa 23.2.1-1ubuntu3.1~22.04.3 via llvmpipe (LLVM 15.0.7, 256 bits))
├☑ pyvistaqt 0.11.2
├☑ vtk 9.3.1
├☑ qtpy 2.4.3 (PyQt6=6.8.2)
└☐ unavailable ipympl, pyqtgraph, mne-qt-browser, ipywidgets, trame_client, trame_server, trame_vtk, trame_vuetify
Ecosystem (optional)
├☑ mne-bids 0.17.0.dev32+g3a25345
├☑ mne-bids-pipeline 1.10.0.dev68+gb995b51
├☑ edfio 0.4.6
├☑ pybv 0.7.6
└☐ unavailable mne-nirs, mne-features, mne-connectivity, mne-icalabel, neo, eeglabio, mffpy