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
Loading, please wait
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
mind0020199108100109119105106104

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