auditory
trial_type subject session left/noise left/tone/2500 left/tone/4000 left/tone/500 right/noise right/tone/2500 right/tone/4000 right/tone/500
mind002 01 99 108 100 109 119 105 106 104

1 rows × 10 columns

General
Filename(s) sub-mind002_ses-01_task-auditory_ave.fif
MNE object type Evoked
Measurement date 1912-10-26 at 16:30:04 UTC
Participant sub-mind002
Experimenter mne_anonymize
Acquisition
Aggregation average of 1 epochs
Condition Grand average: 0.29 × left/noise + 0.24 × left/tone/2500 + 0.15 × left/tone/4000 + 0.32 × left/tone/500
Time range -0.200 – 0.500 s
Baseline -0.200 – 0.000 s
Sampling frequency 1250.00 Hz
Time points 876
Channels
Magnetometers
Gradiometers
Head & sensor digitization 136 points
Filters
Highpass 1.00 Hz
Lowpass 40.00 Hz
Projections PCA-v1 (on)
PCA-v2 (on)
PCA-v3 (on)
planar-ECG--0.500-0.500)-PCA-01 (on)
axial-ECG--0.500-0.500)-PCA-01 (on)
Global field power
General
Filename(s) sub-mind002_ses-01_task-auditory_ave.fif
MNE object type Evoked
Measurement date 1912-10-26 at 16:30:04 UTC
Participant sub-mind002
Experimenter mne_anonymize
Acquisition
Aggregation average of 1 epochs
Condition Grand average: 0.27 × right/noise + 0.18 × right/tone/2500 + 0.29 × right/tone/4000 + 0.27 × right/tone/500
Time range -0.200 – 0.500 s
Baseline -0.200 – 0.000 s
Sampling frequency 1250.00 Hz
Time points 876
Channels
Magnetometers
Gradiometers
Head & sensor digitization 136 points
Filters
Highpass 1.00 Hz
Lowpass 40.00 Hz
Projections PCA-v1 (on)
PCA-v2 (on)
PCA-v3 (on)
planar-ECG--0.500-0.500)-PCA-01 (on)
axial-ECG--0.500-0.500)-PCA-01 (on)
Global field power
<bids_root>/sub-mind002/ses-01/meg/sub-mind002_ses-01_task-auditory_meg.fif<bids_root>/sub-mind002/ses-01/meg/sub-mind002_ses-01_task-auditory_meg.fif<deriv_root>/sub-mind002/ses-01/meg/sub-mind002_ses-01_task-auditory_proc-filt_raw.fif<deriv_root>/sub-mind002/ses-01/meg/sub-mind002_ses-01_task-auditory_proc-clean_epo.fifmeg<bids_root>/sub-mind002/ses-01/meg/sub-mind002_ses-01_task-auditory_meg.fif <bids_root>/sub-mind002/ses-01/meg/sub-mind002_ses-01_task-auditory_meg.jsonbads<deriv_root>/sub-mind002/ses-01/meg/sub-mind002_ses-01_task-auditory_bads.tsv<deriv_root>/sub-mind002/ses-01/meg/sub-mind002_ses-01_task-auditory_proc-filt_raw.fifproc-filt raw<deriv_root>/sub-mind002/ses-01/meg/sub-mind002_ses-01_task-auditory_proc-filt_raw.fifproj<deriv_root>/sub-mind002/ses-01/meg/sub-mind002_ses-01_proj.fifepo<deriv_root>/sub-mind002/ses-01/meg/sub-mind002_ses-01_task-auditory_epo.fifproc-ssp epo<deriv_root>/sub-mind002/ses-01/meg/sub-mind002_ses-01_task-auditory_proc-ssp_epo.fifproc-clean epo<deriv_root>/sub-mind002/ses-01/meg/sub-mind002_ses-01_task-auditory_proc-clean_epo.fifave<deriv_root>/sub-mind002/ses-01/meg/sub-mind002_ses-01_task-auditory_ave.fif<deriv_root>/sub-mind002/ses-01/meg/sub-mind002_ses-01_task-auditory_ave.fifBIDS raw data<bids_root> = /home/circleci/mne_data/ds004107 <deriv_root> = /home/circleci/mne_data/derivatives/mne-bids-pipeline/ds004107init_02_find_empty_roomFind empty-room data matches took 0.1 s completed 2026-09-03 15:13:09 writes: <deriv_root>/sub-mind002/ses-01/meg/sub-mind002_ses-01_task-auditory_emptyroommatch.jsonpreprocessing_01_data_qualityAssess data quality and find bad (and flat) channels took 4.1 s completed 2026-09-03 15:13:13 writes: <deriv_root>/sub-mind002/ses-01/meg/sub-mind002_ses-01_task-auditory_bads.tsv <deriv_root>/sub-mind002/ses-01/meg/sub-mind002_ses-01_task-auditory_scores.jsonpreprocessing_04_frequency_filterApply low- and high-pass filters took 2.2 s completed 2026-09-03 15:13:15 writes: <deriv_root>/sub-mind002/ses-01/meg/sub-mind002_ses-01_task-auditory_proc-filt_raw.fifpreprocessing_06b_run_sspCompute SSP took 2.5 s completed 2026-09-03 15:13:18 writes: <deriv_root>/sub-mind002/ses-01/meg/sub-mind002_ses-01_ecg-epo.fif <deriv_root>/sub-mind002/ses-01/meg/sub-mind002_ses-01_ecg-eve.txt <deriv_root>/sub-mind002/ses-01/meg/sub-mind002_ses-01_proj.fifpreprocessing_07_make_epochsExtract epochs took 2.4 s completed 2026-09-03 15:13:20 writes: <deriv_root>/sub-mind002/ses-01/meg/sub-mind002_ses-01_task-auditory_epo.fifpreprocessing_08b_apply_sspApply SSP took 2.1 s over 2 calls completed 2026-09-03 15:13:22 writes: <deriv_root>/sub-mind002/ses-01/meg/sub-mind002_ses-01_task-auditory_proc-clean_raw.fif <deriv_root>/sub-mind002/ses-01/meg/sub-mind002_ses-01_task-auditory_proc-ssp_epo.fifpreprocessing_09_ptp_rejectRemove epochs based on PTP amplitudes took 2.3 s completed 2026-09-03 15:13:25 writes: <deriv_root>/sub-mind002/ses-01/meg/sub-mind002_ses-01_task-auditory_proc-clean_epo.fifsensor_01_make_evokedExtract evoked data for each condition took 3.9 s completed 2026-09-03 15:13:29 writes: <deriv_root>/sub-mind002/ses-01/meg/sub-mind002_ses-01_task-auditory_ave.fifsensor_06_make_covNoise covariance estimation took 11.4 s completed 2026-09-03 15:13:40 writes: <deriv_root>/sub-mind002/ses-01/meg/sub-mind002_ses-01_proc-clean_cov.fif <deriv_root>/sub-mind002/ses-01/meg/sub-mind002_ses-01_proc-clean_rank.jsonsensor_99_group_averageGroup average at the sensor level
  """ds004107: MIND DATA.

See [OpenNeuro](https://openneuro.org/datasets/ds004107) for more information.

```
M.P. Weisend, F.M. Hanlon, R. Montaño, S.P. Ahlfors, A.C. Leuthold,
D. Pantazis, J.C. Mosher, A.P. Georgopoulos, M.S. Hämäläinen, C.J.
Aine,, V. (2007).
Paving the way for cross-site pooling of magnetoencephalography (MEG) data.
International Congress Series, Volume 1300, Pages 615-618.
```
"""

# This has auditory, median, indx, visual, rest, and emptyroom but let's just
# process the auditory (it's the smallest after rest)
bids_root = "~/mne_data/ds004107"
deriv_root = "~/mne_data/derivatives/mne-bids-pipeline/ds004107"
subjects = ["mind002"]
sessions = ["01"]
conditions = ["left", "right"]  # there are also tone and noise
task = "auditory"
ch_types = ["meg"]
crop_runs = (0, 120)  # to speed up computations
spatial_filter = "ssp"
l_freq = 1.0
h_freq = 40.0

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