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"
mf_int_order = 9
mf_ext_order = 2

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-6.8.0-1040-aws-x86_64-with-glibc2.35
Python               3.13.11 (main, Dec  6 2025, 01:10:48) [GCC 11.4.0]
Executable           /home/circleci/.pyenv/versions/3.13.11/bin/python3.13
CPU                  Intel(R) Xeon(R) Platinum 8124M CPU @ 3.00GHz (36 cores)
Memory               4.0 GiB

Core
├☑ mne               1.12.0.dev117+g39ee092e1 (development, latest release is 1.11.0)
├☑ numpy             2.4.3 (OpenBLAS 0.3.31.dev with 2 threads)
├☑ scipy             1.17.1
└☑ matplotlib        3.10.8 (backend=agg)

Numerical (optional)
├☑ sklearn           1.8.0
├☑ numba             0.64.0
├☑ nibabel           5.4.2
├☑ pandas            3.0.1
├☑ h5io              0.2.5
├☑ h5py              3.16.0
└☐ unavailable       nilearn, dipy, openmeeg, cupy

Visualization (optional)
├☑ pyvista           0.47.1 (OpenGL 4.5 (Core Profile) Mesa 23.2.1-1ubuntu3.1~22.04.3 via llvmpipe (LLVM 15.0.7, 256 bits))
├☑ pyvistaqt         0.11.3
├☑ vtk               9.6.0
├☑ qtpy              2.4.3 (PySide6=6.10.2)
└☐ unavailable       ipympl, pyqtgraph, mne-qt-browser, ipywidgets, trame_client, trame_server, trame_vtk, trame_vuetify

Ecosystem (optional)
├☑ mne-bids          0.19.0.dev28+g6dcc45d4c
├☑ mne-icalabel      0.8.1
├☑ mne-bids-pipeline 1.10.0.dev171+g19a09516e
├☑ eeglabio          0.1.3
├☑ edfio             0.4.13
├☑ curryreader       0.1.2
├☑ pybv              0.7.6
├☑ defusedxml        0.7.1
└☐ unavailable       mne-nirs, mne-features, mne-connectivity, neo, mffpy, pymef, antio