General | ||
---|---|---|
Filename(s) | sub-0001_task-AEF_ave.fif | |
MNE object type | Evoked | |
Measurement date | 1925-01-01 at 09:43:00 UTC | |
Participant | sub-0001 | |
Experimenter | EAB | |
Acquisition | ||
Aggregation | average of 1 epochs | |
Condition | Grand average: standard | |
Time range | -0.200 – 0.500 s | |
Baseline | -0.200 – 0.000 s | |
Sampling frequency | 600.00 Hz | |
Time points | 421 | |
Channels | ||
Magnetometers | ||
Reference Magnetometers | ||
Head & sensor digitization | 8 points | |
Filters | ||
Highpass | 0.30 Hz | |
Lowpass | 100.00 Hz |
General | ||
---|---|---|
Filename(s) | sub-0001_task-AEF_ave.fif | |
MNE object type | Evoked | |
Measurement date | 1925-01-01 at 09:43:00 UTC | |
Participant | sub-0001 | |
Experimenter | EAB | |
Acquisition | ||
Aggregation | average of 1 epochs | |
Condition | Grand average: deviant | |
Time range | -0.200 – 0.500 s | |
Baseline | -0.200 – 0.000 s | |
Sampling frequency | 600.00 Hz | |
Time points | 421 | |
Channels | ||
Magnetometers | ||
Reference Magnetometers | ||
Head & sensor digitization | 8 points | |
Filters | ||
Highpass | 0.30 Hz | |
Lowpass | 100.00 Hz |
General | ||
---|---|---|
Filename(s) | sub-0001_task-AEF_ave.fif | |
MNE object type | Evoked | |
Measurement date | 1925-01-01 at 09:43:00 UTC | |
Participant | sub-0001 | |
Experimenter | EAB | |
Acquisition | ||
Aggregation | average of 1 epochs | |
Condition | Grand average: deviant - standard | |
Time range | -0.200 – 0.500 s | |
Baseline | -0.200 – 0.000 s | |
Sampling frequency | 600.00 Hz | |
Time points | 421 | |
Channels | ||
Magnetometers | ||
Reference Magnetometers | ||
Head & sensor digitization | 8 points | |
Filters | ||
Highpass | 0.30 Hz | |
Lowpass | 100.00 Hz |
"""Brainstorm - Auditory Dataset.
See https://openneuro.org/datasets/ds000246/versions/1.0.0 for more
information.
"""
bids_root = "~/mne_data/ds000246"
deriv_root = "~/mne_data/derivatives/mne-bids-pipeline/ds000246"
runs = ["01"]
crop_runs = (0, 120) # Reduce memory usage on CI system
read_raw_bids_verbose = "error" # No BIDS -> MNE mapping found for channel ...
l_freq = 0.3
h_freq = 100
epochs_decim = 4
subjects = ["0001"]
ch_types = ["meg"]
reject = dict(mag=4e-12, eog=250e-6)
conditions = ["standard", "deviant", "button"]
epochs_metadata_tmin = ["standard", "deviant"] # for testing only
contrasts = [("deviant", "standard")]
decode = True
decoding_time_generalization = True
decoding_time_generalization_decim = 4
on_error = "abort"
plot_psd_for_runs = [] # too much memory on CIs
parallel_backend = "dask"
dask_worker_memory_limit = "2G"
dask_temp_dir = "./.dask-worker-space"
dask_open_dashboard = True
n_jobs = 2
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 69.1 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
├☑ eeglabio 0.0.2-4
├☑ edfio 0.4.2
├☑ pybv 0.7.5
└☐ unavailable mne-nirs, mne-features, mne-connectivity, mne-icalabel, neo, mffpy