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
Global field power
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
Time course (Magnetometers)
Time course (Gradiometers)
Global field power
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
Time course (Magnetometers)
Time course (Gradiometers)
Global field power
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
Time course (Magnetometers)
Time course (Gradiometers)
Global field power
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
Time course (Magnetometers)
Time course (Gradiometers)
Global field power
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
Time course (Magnetometers)
Time course (Gradiometers)
Global field power
Full-epochs decoding
Based on N=1 subjects. Each dot represents the mean cross-validation score for a single subject. The dashed line is expected chance performance.
Time generalization: Famous vs. Scrambled
Time generalization (generalization across time, GAT): each classifier is trained on each time point, and tested on all other time points. The results were averaged across N=1 subjects.
Time generalization: Unfamiliar vs. Scrambled
Time generalization (generalization across time, GAT): each classifier is trained on each time point, and tested on all other time points. The results were averaged across N=1 subjects.
Time generalization: Famous vs. Unfamiliar
Time generalization (generalization across time, GAT): each classifier is trained on each time point, and tested on all other time points. The results were averaged across N=1 subjects.
  """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