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
Time course (Magnetometers)
Global field power
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
Global field power
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: button
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
Global field power
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
Time course (Magnetometers)
Global field power
Full-epochs decoding
Based on N=1 subjects. Each dot represents the mean cross-validation score for a single subject. The dashed line is expected chance performance.
Time generalization: deviant vs. standard
Time generalization (generalization across time, GAT): each classifier is trained on each time point, and tested on all other time points. The results were averaged across N=1 subjects.
  """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