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
  """MIND DATA.

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

  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               68.6 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