somato
trial_type subject somato_event1
01 111

1 rows × 2 columns

General
Filename(s) sub-01_task-somato_ave.fif
MNE object type Evoked
Measurement date 1915-03-31 at 00:00:00 UTC
Participant sub-01
Experimenter mne_anonymize
Acquisition
Aggregation average of 1 epochs
Condition Grand average: somato_event1
Time range -0.200 – 0.499 s
Baseline -0.200 – 0.000 s
Sampling frequency 300.31 Hz
Time points 211
Channels
Magnetometers
Gradiometers
Head & sensor digitization 47 points
Filters
Highpass 0.10 Hz
Lowpass 40.00 Hz
Time course (Magnetometers)
Time course (Gradiometers)
Global field power
<deriv_root>/sub-01/meg/sub-01_task-somato_ave.fif<deriv_root>/sub-01/meg/sub-01_task-somato_ave.fifproc-clean cov rank<deriv_root>/sub-01/meg/sub-01_proc-clean_cov.fif <deriv_root>/sub-01/meg/sub-01_proc-clean_rank.json<bids_root>/sub-01/meg/sub-01_task-somato_meg.fif<bids_root>/sub-01/meg/sub-01_task-somato_meg.fif<deriv_root>/sub-01/meg/sub-01_task-somato_proc-clean_epo.fif<fs_subjects_dir>/01/bem/01-oct6-src.fif<deriv_root>/sub-01/meg/sub-01_fwd.fifmeg<bids_root>/sub-01/meg/sub-01_task-somato_meg.fif <bids_root>/sub-01/meg/sub-01_task-somato_meg.json.mgz<fs_subjects_dir>/01/mri/T1.mgz.white<fs_subjects_dir>/01/surf/rh.whitebads<deriv_root>/sub-01/meg/sub-01_task-somato_bads.tsvproc-filt raw<deriv_root>/sub-01/meg/sub-01_task-somato_proc-filt_raw.fifepo<deriv_root>/sub-01/meg/sub-01_task-somato_epo.fifproc-clean epo<deriv_root>/sub-01/meg/sub-01_task-somato_proc-clean_epo.fifave<deriv_root>/sub-01/meg/sub-01_task-somato_ave.fif<deriv_root>/sub-01/meg/sub-01_task-somato_ave.fif.surf<fs_subjects_dir>/01/bem/inner_skull.surf.fif<fs_subjects_dir>/01/bem/01-5120-bem-sol.fiffwd<deriv_root>/sub-01/meg/sub-01_fwd.fifsomatoevent1+dSPM+hemi<deriv_root>/sub-01/meg/sub-01_task-somato_somatoevent1+dSPM+hemi.h5BIDS raw data<bids_root> = /home/circleci/mne_data/ds003104 <deriv_root> = /home/circleci/mne_data/derivatives/mne-bids-pipeline/ds003104 <fs_subjects_dir> = /home/circleci/mne_data/ds003104/derivatives/freesurfer/subjectsBIDS raw data<bids_root> = /home/circleci/mne_data/ds003104 <deriv_root> = /home/circleci/mne_data/derivatives/mne-bids-pipeline/ds003104 <fs_subjects_dir> = /home/circleci/mne_data/ds003104/derivatives/freesurfer/subjectsinit_02_find_empty_roomFind empty-room data matches took 0.0 s completed 2026-09-03 15:14:07 writes: <deriv_root>/sub-01/meg/sub-01_task-somato_emptyroommatch.jsonpreprocessing_01_data_qualityAssess data quality and find bad (and flat) channels took 4.1 s completed 2026-09-03 15:14:11 writes: <deriv_root>/sub-01/meg/sub-01_task-somato_bads.tsv <deriv_root>/sub-01/meg/sub-01_task-somato_scores.jsonpreprocessing_04_frequency_filterApply low- and high-pass filters took 4.0 s completed 2026-09-03 15:14:15 writes: <deriv_root>/sub-01/meg/sub-01_task-somato_proc-filt_raw.fifpreprocessing_07_make_epochsExtract epochs took 2.1 s completed 2026-09-03 15:14:17 writes: <deriv_root>/sub-01/meg/sub-01_task-somato_epo.fifpreprocessing_09_ptp_rejectRemove epochs based on PTP amplitudes took 1.7 s completed 2026-09-03 15:14:19 writes: <deriv_root>/sub-01/meg/sub-01_task-somato_proc-clean_epo.fifsensor_01_make_evokedExtract evoked data for each condition took 1.9 s completed 2026-09-03 15:14:21 writes: <deriv_root>/sub-01/meg/sub-01_task-somato_ave.fifsensor_06_make_covNoise covariance estimation took 6.0 s completed 2026-09-03 15:14:27 writes: <deriv_root>/sub-01/meg/sub-01_proc-clean_cov.fif <deriv_root>/sub-01/meg/sub-01_proc-clean_rank.jsonsensor_99_group_averageGroup average at the sensor level took 2.0 s completed 2026-09-03 15:14:29 writes: <deriv_root>/sub-average/meg/sub-average_task-somato_proc-clean_ave.fifsource_01_make_bem_surfacesCreate BEM surfaces cached (original run not recorded) writes: <fs_subjects_dir>/01/bem/inner_skull.surfsource_02_make_bem_solutionCompute BEM solution took 6.0 s completed 2026-09-03 15:14:35 writes: <fs_subjects_dir>/01/bem/01-5120-bem-sol.fif <fs_subjects_dir>/01/bem/01-5120-bem.fifsource_03_setup_source_spaceSetup source space took 0.6 s completed 2026-09-03 15:14:36 writes: <fs_subjects_dir>/01/bem/01-oct6-src.fifsource_04_make_forwardForward solution took 23.3 s completed 2026-09-03 15:14:59 writes: <deriv_root>/sub-01/meg/sub-01_fwd.fif <deriv_root>/sub-01/meg/sub-01_trans.fifsource_05_make_inverseInverse solution took 16.2 s over 2 calls completed 2026-09-03 15:15:15 writes: <deriv_root>/sub-01/meg/sub-01_inv.fif <deriv_root>/sub-01/meg/sub-01_task-somato_somatoevent1+dSPM+hemi.h5source_99_group_averageGroup average at the source level took 9.6 s completed 2026-09-03 15:15:25 writes: <deriv_root>/sub-01/meg/sub-01_task-somato_somatoevent1+dSPM+morph2fsaverage+hemi.h5
  """ds003104: Somatosensory.

See [OpenNeuro](https://openneuro.org/datasets/ds003104) for more information.
"""

bids_root = "~/mne_data/ds003104"
deriv_root = "~/mne_data/derivatives/mne-bids-pipeline/ds003104"
subjects_dir = f"{bids_root}/derivatives/freesurfer/subjects"

conditions = ["somato_event1"]
ch_types = ["meg"]

report_image_format = dict(raster="png")

  Platform             Linux-7.0.0-1004-aws-x86_64-with-glibc2.43 (X11)
Python               3.14.4 (main, May 12 2026, 13:57:53) [GCC 15.2.0]
Executable           /home/circleci/python_env/bin/python3
CPU                  AMD EPYC 9R45 (2 cores)
Memory               7.6 GiB

Core
├☑ mne               1.13.0.dev325+g166e95903 (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, jamica, cupy

Visualization (optional)
├☑ pyvista           0.48.4 (OpenGL 4.5 (Core Profile) Mesa 26.0.8-1ubuntu0.3 via llvmpipe (LLVM 21.1.8, 256 bits))
├☑ pyvistaqt         0.13.0
├☑ 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.dev38+g6784b4c8e
├☑ mne-icalabel      0.9.0
├☑ mne-bids-pipeline 1.11.0.dev74+g91c2e0677
├☑ autoreject        0.5.0
├☑ 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