General
Measurement date August 24, 2021 10:56:23 GMT
Experimenter Unknown
Participant <no
Channels
Digitized points Not available
Good channels 64 EEG
Bad channels None
EOG channels Not available
ECG channels Not available
Data
Sampling frequency 1024.00 Hz
Highpass 0.00 Hz
Lowpass 512.00 Hz
Filenames sub-010_ses-t1_task-resteyesc_eeg.edf
Duration 00:03:60 (HH:MM:SS)
PSD
General
Measurement date May 09, 2017 12:11:44 GMT
Experimenter mne_anonymize
Participant sub-010
Channels
Digitized points Not available
Good channels 64 EEG
Bad channels None
EOG channels Not available
ECG channels Not available
Data
Sampling frequency 1024.00 Hz
Highpass 0.00 Hz
Lowpass 40.00 Hz
Filenames sub-010_ses-t1_task-resteyesc_eeg.edf
Duration 00:03:60 (HH:MM:SS)
PSD
Number of events 22
Events rest: 22
Time range 0.000 – 10.000 s
Baseline off
No epochs exceeded the rejection thresholds. Nothing was dropped.
PSD
PSD calculated from 3 epochs (30.0 s).
{}
Number of events 22
Events rest: 22
Time range 0.000 – 10.000 s
Baseline off
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-5.15.0-1053-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               1.7.0.dev156+g415e7f68e (devel, latest release is 1.6.1)
├☑ numpy             1.26.4 (OpenBLAS 0.3.23.dev with 2 threads)
├☑ scipy             1.12.0
└☑ matplotlib        3.8.3 (backend=agg)

Numerical (optional)
├☑ sklearn           1.4.1.post1
├☑ numba             0.59.1
├☑ nibabel           5.2.1
├☑ pandas            2.2.1
└☐ unavailable       nilearn, dipy, openmeeg, cupy

Visualization (optional)
├☑ pyvista           0.43.4 (OpenGL 4.5 (Core Profile) Mesa 23.2.1-1ubuntu3.1~22.04.2 via llvmpipe (LLVM 15.0.7, 256 bits))
├☑ pyvistaqt         0.11.0
├☑ vtk               9.3.0
├☑ qtpy              2.4.1 (PyQt6=6.6.0)
└☐ unavailable       ipympl, pyqtgraph, mne-qt-browser, ipywidgets, trame_client, trame_server, trame_vtk, trame_vuetify

Ecosystem (optional)
├☑ mne-bids          0.15.0.dev43+g17d20c132
├☑ mne-bids-pipeline 1.8.0
└☐ unavailable       mne-nirs, mne-features, mne-connectivity, mne-icalabel, neo