General
Filename(s) sub-0001_task-AEF_ave.fif
MNE object type Evoked
Measurement date 1925-01-01 at 09:43:00 UTC
Participant sub-0001
Experimenter EAB
Acquisition
Aggregation average of 1 epochs
Condition Grand average: standard
Time range -0.200 – 0.500 s
Baseline -0.200 – 0.000 s
Sampling frequency 600.00 Hz
Time points 421
Channels
Magnetometers
Reference Magnetometers
Head & sensor digitization 8 points
Filters
Highpass 0.30 Hz
Lowpass 100.00 Hz
Time course (Magnetometers)
Global field power
General
Filename(s) sub-0001_task-AEF_ave.fif
MNE object type Evoked
Measurement date 1925-01-01 at 09:43:00 UTC
Participant sub-0001
Experimenter EAB
Acquisition
Aggregation average of 1 epochs
Condition Grand average: deviant
Time range -0.200 – 0.500 s
Baseline -0.200 – 0.000 s
Sampling frequency 600.00 Hz
Time points 421
Channels
Magnetometers
Reference Magnetometers
Head & sensor digitization 8 points
Filters
Highpass 0.30 Hz
Lowpass 100.00 Hz
Global field power
General
Filename(s) sub-0001_task-AEF_ave.fif
MNE object type Evoked
Measurement date 1925-01-01 at 09:43:00 UTC
Participant sub-0001
Experimenter EAB
Acquisition
Aggregation average of 1 epochs
Condition Grand average: button
Time range -0.200 – 0.500 s
Baseline -0.200 – 0.000 s
Sampling frequency 600.00 Hz
Time points 421
Channels
Magnetometers
Reference Magnetometers
Head & sensor digitization 8 points
Filters
Highpass 0.30 Hz
Lowpass 100.00 Hz
Global field power
General
Filename(s) sub-0001_task-AEF_ave.fif
MNE object type Evoked
Measurement date 1925-01-01 at 09:43:00 UTC
Participant sub-0001
Experimenter EAB
Acquisition
Aggregation average of 1 epochs
Condition Grand average: deviant - standard
Time range -0.200 – 0.500 s
Baseline -0.200 – 0.000 s
Sampling frequency 600.00 Hz
Time points 421
Channels
Magnetometers
Reference Magnetometers
Head & sensor digitization 8 points
Filters
Highpass 0.30 Hz
Lowpass 100.00 Hz
Time course (Magnetometers)
Global field power
Full-epochs decoding
Based on effective N=1 subjects. Each dot represents the mean cross-validation score for a single subject. The dashed line is expected chance performance.
Decoding over time: deviant vs. standard
Based on N=1 subjects. Standard error and confidence interval of the mean were bootstrapped with 5000 resamples. CI must not be used for statistical inference here, as it is not corrected for multiple testing.
Time generalization: deviant vs. standard
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.
<bids_root>/sub-0001/meg/sub-0001_task-AEF_run-01_meg.ds<deriv_root>/sub-0001/meg/sub-0001_task-AEF_epo.fif<bids_root>/sub-0001/meg/sub-0001_task-AEF_run-01_meg.ds<deriv_root>/sub-0001/meg/sub-0001_task-AEF_proc-clean_epo.fifmegrun 01<bids_root>/sub-0001/meg/sub-0001_task-AEF_run-01_meg.ds <bids_root>/sub-0001/meg/sub-0001_task-AEF_run-01_meg.jsonbadsrun 01<deriv_root>/sub-0001/meg/sub-0001_task-AEF_run-01_bads.tsvproc-filt rawrun 01<deriv_root>/sub-0001/meg/sub-0001_task-AEF_run-01_proc-filt_raw.fifepo<deriv_root>/sub-0001/meg/sub-0001_task-AEF_epo.fif<deriv_root>/sub-0001/meg/sub-0001_task-AEF_proc-clean_epo.fif<deriv_root>/sub-0001/meg/sub-0001_task-AEF_proc-clean_epo.fifproc-clean epo<deriv_root>/sub-0001/meg/sub-0001_task-AEF_proc-clean_epo.fif<deriv_root>/sub-0001/meg/sub-0001_task-AEF_ave.fifave<deriv_root>/sub-0001/meg/sub-0001_task-AEF_ave.fifproc-deviant+standard+F… decoding<deriv_root>/sub-0001/meg/sub-0001_task-AEF_proc-deviant+standard+FullEpochs+rocauc_decoding.matproc-deviant+standard+T… decoding<deriv_root>/sub-0001/meg/sub-0001_task-AEF_proc-deviant+standard+TimeByTime+rocauc_decoding.matBIDS raw data<bids_root> = /home/circleci/mne_data/ds000246 <deriv_root> = /home/circleci/mne_data/derivatives/mne-bids-pipeline/ds000246init_02_find_empty_roomFind empty-room data matches took 0.0 s completed 2026-08-28 03:34:06 writes: <deriv_root>/sub-0001/meg/sub-0001_task-AEF_run-01_emptyroommatch.jsonpreprocessing_01_data_qualityAssess data quality and find bad (and flat) channels took 26.9 s completed 2026-08-28 03:34:36 writes: <deriv_root>/sub-0001/meg/sub-0001_task-AEF_run-01_bads.tsv <deriv_root>/sub-0001/meg/sub-0001_task-AEF_run-01_scores.jsonpreprocessing_04_frequency_filterApply low- and high-pass filters took 24.5 s completed 2026-08-28 03:35:05 writes: <deriv_root>/sub-0001/meg/sub-0001_task-AEF_run-01_proc-filt_raw.fifpreprocessing_07_make_epochsExtract epochs took 10.8 s completed 2026-08-28 03:35:16 writes: <deriv_root>/sub-0001/meg/sub-0001_task-AEF_epo.fifpreprocessing_09_ptp_rejectRemove epochs based on PTP amplitudes took 8.2 s completed 2026-08-28 03:35:24 writes: <deriv_root>/sub-0001/meg/sub-0001_task-AEF_proc-clean_epo.fifsensor_01_make_evokedExtract evoked data for each condition took 48.7 s completed 2026-08-28 03:36:13 writes: <deriv_root>/sub-0001/meg/sub-0001_task-AEF_ave.fifsensor_02_decoding_full_epochsDecode pairs of conditions based on entire epochs took 5.4 s completed 2026-08-28 03:36:19 writes: <deriv_root>/sub-0001/meg/sub-0001_task-AEF_proc-deviant+standard+FullEpochs+rocauc_decoding.mat <deriv_root>/sub-0001/meg/sub-0001_task-AEF_proc-deviant+standard+FullEpochs+rocauc_decoding.tsvsensor_03_decoding_time_by_timeDecode time-by-time using a "sliding" estimator took 38.2 s completed 2026-08-28 03:36:57 writes: <deriv_root>/sub-0001/meg/sub-0001_task-AEF_proc-deviant+standard+TimeByTime+rocauc_decoding.mat <deriv_root>/sub-0001/meg/sub-0001_task-AEF_proc-deviant+standard+TimeByTime+rocauc_decoding.tsvsensor_06_make_covNoise covariance estimation took 17.6 s completed 2026-08-28 03:37:15 writes: <deriv_root>/sub-0001/meg/sub-0001_proc-clean_cov.fif <deriv_root>/sub-0001/meg/sub-0001_proc-clean_rank.jsonsensor_99_group_averageGroup average at the sensor level took 43.4 s over 3 calls completed 2026-08-28 03:37:58 writes: <deriv_root>/sub-average/meg/sub-average_task-AEF_proc-FullEpochs+rocauc_decoding.xlsx <deriv_root>/sub-average/meg/sub-average_task-AEF_proc-clean_ave.fif <deriv_root>/sub-average/meg/sub-average_task-AEF_proc-deviant+standard+FullEpochs+rocauc_decoding.mat
  """ds000246: Brainstorm Auditory MEG.

See [OpenNeuro](https://openneuro.org/datasets/ds000246) for more information.
"""

import sys

bids_root = "~/mne_data/ds000246"
deriv_root = "~/mne_data/derivatives/mne-bids-pipeline/ds000246"

runs = ["01"]
crop_runs = (0, 120)  # Reduce memory usage on CI system
l_freq = 0.3
h_freq = 100
epochs_decim = 4
subjects = ["0001"]
ch_types = ["meg"]
reject = dict(mag=4e-12, eog=250e-6)
conditions = ["standard", "deviant", "button"]
epochs_metadata_tmin = ["standard", "deviant"]  # for testing only
contrasts = [("deviant", "standard")]
decode = True
decoding_time_generalization = True
decoding_time_generalization_decim = 4
on_error = "abort"
plot_psd_for_runs = []  # too much memory on CIs

parallel_backend = "dask"
dask_worker_memory_limit = "3G" if sys.platform == "darwin" else "2G"
dask_temp_dir = "./.dask-worker-space"
dask_open_dashboard = True
n_jobs = 2

  Platform             Linux-7.0.0-1009-aws-x86_64-with-glibc2.39 (X11)
Python               3.14.5 (main, May 12 2026, 13:13:59) [GCC 13.3.0]
Executable           /home/circleci/.pyenv/versions/3.14.5/bin/python3.14
CPU                  Intel(R) Xeon(R) Platinum 8124M CPU @ 3.00GHz (36 cores)
Memory               4.0 GiB

Core
├☑ mne               1.13.0.dev294+ga0eb9250f (development, latest release is 1.12.1)
├☑ numpy             2.5.2 (OpenBLAS 0.3.34.0.0 with 2 threads via pthreads)
├☑ scipy             1.18.1
└☑ matplotlib        3.11.1 (backend=agg)

Numerical (optional)
├☑ scikit-learn      1.9.0
├☑ threadpoolctl     3.6.0
├☑ numba             0.67.0
├☑ nibabel           5.4.2
├☑ pandas            3.0.5
├☑ h5io              0.2.5
├☑ h5py              3.16.0
└☐ unavailable       nilearn, dipy, openmeeg, python-picard, cupy

Visualization (optional)
├☑ pyvista           0.48.4 (OpenGL 4.5 (Core Profile) Mesa 25.2.8-0ubuntu0.24.04.2 via llvmpipe (LLVM 20.1.2, 256 bits))
├☑ pyvistaqt         0.12.0
├☑ vtk               9.6.2
├☑ qtpy              2.4.3 (PySide6=6.11.2)
└☐ unavailable       ipympl, pyqtgraph, mne-qt-browser, ipywidgets, trame, trame_client, trame_server, trame_pyvista, trame_vtk, trame_vuetify

Ecosystem (optional)
├☑ mne-bids          0.20.0.dev34+g25f78ddf4
├☑ mne-icalabel      0.9.0
├☑ mne-bids-pipeline 1.11.0.dev68+g1c2c2d8b2
├☑ autoreject        0.5.0.dev5+gb4e218e6
├☑ eeglabio          0.1.3
├☑ edfio             0.4.16
├☑ curryreader       0.1.2
├☑ mffpy             0.11.0
├☑ pybv              0.8.1
├☑ defusedxml        0.7.1
└☐ unavailable       mne-nirs, mne-features, mne-connectivity, neo, pymef, antio