| 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) |
|
| 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) |
|
"""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-6.8.0-1039-aws-x86_64-with-glibc2.35
Python 3.12.4 (main, Jun 8 2024, 23:40:19) [GCC 11.4.0]
Executable /home/circleci/.pyenv/versions/3.12.4/bin/python3.12
CPU Intel(R) Xeon(R) Platinum 8223CL CPU @ 3.00GHz (36 cores)
Memory 8.0 GiB
Core
├☑ mne 1.11.0.dev90+ge50d56542 (development, latest release is 1.10.2)
├☑ numpy 2.3.4 (OpenBLAS 0.3.30 with 2 threads)
├☑ scipy 1.16.2
└☑ matplotlib 3.10.7 (backend=agg)
Numerical (optional)
├☑ sklearn 1.7.2
├☑ numba 0.62.1
├☑ nibabel 5.3.2
├☑ pandas 2.3.3
├☑ h5io 0.2.5
├☑ h5py 3.15.1
└☐ unavailable nilearn, dipy, openmeeg, cupy
Visualization (optional)
├☑ pyvista 0.46.3 (OpenGL 4.5 (Core Profile) Mesa 23.2.1-1ubuntu3.1~22.04.3 via llvmpipe (LLVM 15.0.7, 256 bits))
├☑ pyvistaqt 0.11.3
├☑ vtk 9.5.2
├☑ qtpy 2.4.3 (PyQt6=6.10.0)
└☐ unavailable ipympl, pyqtgraph, mne-qt-browser, ipywidgets, trame_client, trame_server, trame_vtk, trame_vuetify
Ecosystem (optional)
├☑ mne-bids 0.18.0.dev23+g982570293
├☑ mne-bids-pipeline 1.10.0.dev113+gfd73224c5
├☑ eeglabio 0.1.2
├☑ edfio 0.4.10
├☑ pybv 0.7.6
├☑ defusedxml 0.7.1
└☐ unavailable mne-nirs, mne-features, mne-connectivity, mne-icalabel, neo, mffpy, antio