| 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-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 8124M CPU @ 3.00GHz (36 cores)
Memory 4.0 GiB
Core
├☑ mne 1.11.0.dev77+g3cfac64bb (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=6.9.0)
└☐ unavailable ipympl, pyqtgraph, mne-qt-browser, ipywidgets, trame_client, trame_server, trame_vtk, trame_vuetify
Ecosystem (optional)
├☑ mne-bids 0.18.0.dev20+g9098c6e6b
├☑ mne-bids-pipeline 1.10.0.dev110+gd5d3b02fe
├☑ 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