facerecognition | |||||||
---|---|---|---|---|---|---|---|
trial_type | subject | session | run | Famous | Scrambled | Unfamiliar | |
01 | meg | 01 | 49 | 50 | 47 | ||
01 | meg | 02 | 49 | 50 | 49 | ||
01 | meg | 03 | 50 | 50 | 50 | ||
01 | meg | 04 | 48 | 49 | 50 | ||
01 | meg | 05 | 49 | 49 | 50 | ||
01 | meg | 06 | 50 | 49 | 49 |
6 rows × 6 columns
General | ||
---|---|---|
Filename(s) | sub-01_ses-meg_task-facerecognition_ave.fif | |
MNE object type | Evoked | |
Measurement date | 2009-04-09 at 12:04:14 UTC | |
Participant | sub-01 | |
Experimenter | mne_anonymize | |
Acquisition | ||
Aggregation | average of 1 epochs | |
Condition | Grand average: Famous | |
Time range | -0.200 – 0.496 s | |
Baseline | -0.200 – 0.000 s | |
Sampling frequency | 125.00 Hz | |
Time points | 88 | |
Channels | ||
Magnetometers | ||
Gradiometers | ||
Head & sensor digitization | 137 points | |
Filters | ||
Highpass | 0.00 Hz | |
Lowpass | 40.00 Hz |
General | ||
---|---|---|
Filename(s) | sub-01_ses-meg_task-facerecognition_ave.fif | |
MNE object type | Evoked | |
Measurement date | 2009-04-09 at 12:04:14 UTC | |
Participant | sub-01 | |
Experimenter | mne_anonymize | |
Acquisition | ||
Aggregation | average of 1 epochs | |
Condition | Grand average: Unfamiliar | |
Time range | -0.200 – 0.496 s | |
Baseline | -0.200 – 0.000 s | |
Sampling frequency | 125.00 Hz | |
Time points | 88 | |
Channels | ||
Magnetometers | ||
Gradiometers | ||
Head & sensor digitization | 137 points | |
Filters | ||
Highpass | 0.00 Hz | |
Lowpass | 40.00 Hz |
General | ||
---|---|---|
Filename(s) | sub-01_ses-meg_task-facerecognition_ave.fif | |
MNE object type | Evoked | |
Measurement date | 2009-04-09 at 12:04:14 UTC | |
Participant | sub-01 | |
Experimenter | mne_anonymize | |
Acquisition | ||
Aggregation | average of 1 epochs | |
Condition | Grand average: Scrambled | |
Time range | -0.200 – 0.496 s | |
Baseline | -0.200 – 0.000 s | |
Sampling frequency | 125.00 Hz | |
Time points | 88 | |
Channels | ||
Magnetometers | ||
Gradiometers | ||
Head & sensor digitization | 137 points | |
Filters | ||
Highpass | 0.00 Hz | |
Lowpass | 40.00 Hz |
General | ||
---|---|---|
Filename(s) | sub-01_ses-meg_task-facerecognition_ave.fif | |
MNE object type | Evoked | |
Measurement date | 2009-04-09 at 12:04:14 UTC | |
Participant | sub-01 | |
Experimenter | mne_anonymize | |
Acquisition | ||
Aggregation | average of 1 epochs | |
Condition | Grand average: Famous - Scrambled | |
Time range | -0.200 – 0.496 s | |
Baseline | -0.200 – 0.000 s | |
Sampling frequency | 125.00 Hz | |
Time points | 88 | |
Channels | ||
Magnetometers | ||
Gradiometers | ||
Head & sensor digitization | 137 points | |
Filters | ||
Highpass | 0.00 Hz | |
Lowpass | 40.00 Hz |
General | ||
---|---|---|
Filename(s) | sub-01_ses-meg_task-facerecognition_ave.fif | |
MNE object type | Evoked | |
Measurement date | 2009-04-09 at 12:04:14 UTC | |
Participant | sub-01 | |
Experimenter | mne_anonymize | |
Acquisition | ||
Aggregation | average of 1 epochs | |
Condition | Grand average: Unfamiliar - Scrambled | |
Time range | -0.200 – 0.496 s | |
Baseline | -0.200 – 0.000 s | |
Sampling frequency | 125.00 Hz | |
Time points | 88 | |
Channels | ||
Magnetometers | ||
Gradiometers | ||
Head & sensor digitization | 137 points | |
Filters | ||
Highpass | 0.00 Hz | |
Lowpass | 40.00 Hz |
General | ||
---|---|---|
Filename(s) | sub-01_ses-meg_task-facerecognition_ave.fif | |
MNE object type | Evoked | |
Measurement date | 2009-04-09 at 12:04:14 UTC | |
Participant | sub-01 | |
Experimenter | mne_anonymize | |
Acquisition | ||
Aggregation | average of 1 epochs | |
Condition | Grand average: Famous - Unfamiliar | |
Time range | -0.200 – 0.496 s | |
Baseline | -0.200 – 0.000 s | |
Sampling frequency | 125.00 Hz | |
Time points | 88 | |
Channels | ||
Magnetometers | ||
Gradiometers | ||
Head & sensor digitization | 137 points | |
Filters | ||
Highpass | 0.00 Hz | |
Lowpass | 40.00 Hz |
"""Faces dataset."""
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
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 69.1 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