General
Filename(s) sub-010_ses-t1_task-resteyesc_eeg.edf
MNE object type RawEDF
Measurement date 2017-05-09 at 12:11:44 UTC
Participant sub-010
Experimenter mne_anonymize
Acquisition
Duration 00:03:60 (HH:MM:SS)
Sampling frequency 1024.00 Hz
Time points 245,760
Channels
EEG
Head & sensor digitization Not available
Filters
Highpass 0.00 Hz
Lowpass 512.00 Hz
PSD
General
Filename(s) sub-010_ses-t1_task-resteyesc_eeg.edf
MNE object type RawEDF
Measurement date 2017-05-09 at 12:11:44 UTC
Participant sub-010
Experimenter mne_anonymize
Acquisition
Duration 00:03:60 (HH:MM:SS)
Sampling frequency 1024.00 Hz
Time points 245,760
Channels
EEG
Head & sensor digitization Not available
Filters
Highpass 0.00 Hz
Lowpass 40.00 Hz
PSD
General
MNE object type Epochs
Measurement date 2017-05-09 at 12:11:44 UTC
Participant sub-010
Experimenter mne_anonymize
Acquisition
Total number of events 22
Events counts rest: 22
Time range 0.000 – 10.000 s
Baseline off
Sampling frequency 1024.00 Hz
Time points 10,241
Channels
EEG
Head & sensor digitization Not available
Filters
Highpass 0.00 Hz
Lowpass 40.00 Hz
Projections Average EEG reference (off)
No epochs exceeded the rejection thresholds. Nothing was dropped.
PSD
PSD calculated from 3 epochs (30.0 s).
{}
General
Filename(s) sub-010_ses-t1_task-resteyesc_epo.fif
MNE object type EpochsFIF
Measurement date 2017-05-09 at 12:11:44 UTC
Participant sub-010
Experimenter mne_anonymize
Acquisition
Total number of events 22
Events counts rest: 22
Time range 0.000 – 10.000 s
Baseline off
Sampling frequency 1024.00 Hz
Time points 10,241
Channels
EEG
Head & sensor digitization Not available
Filters
Highpass 0.00 Hz
Lowpass 40.00 Hz
Projections Average EEG reference (on)
ERP image (EEG)
No epochs exceeded the rejection thresholds. Nothing was dropped.
PSD
PSD calculated from 3 epochs (30.0 s).
  """SRM Resting-state EEG."""

bids_root = "~/mne_data/ds003775"
deriv_root = "~/mne_data/derivatives/mne-bids-pipeline/ds003775"

# To get all subjects for example:
# from mne_bids import get_entity_vals
# subjects = sorted(get_entity_vals(bids_root, entity_key='subject'))
subjects = ["010"]

reader_extra_params = {"units": "uV"}

sessions = ["t1"]

run_source_estimation = False

ch_types = ["eeg"]

baseline = None
reject = None
spatial_filter = None

h_freq = 40
l_freq = None

task = "resteyesc"
task_is_rest = True
epochs_tmin = 0.0
epochs_tmax = 10.0
rest_epochs_overlap = 0.0
rest_epochs_duration = 10.0
baseline = None

parallel_backend = "loky"
dask_open_dashboard = True

log_level = "info"

n_jobs = 1

  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