audiovisual
trial_type subject run Auditory/Left Auditory/Right Button Smiley Visual/Left Visual/Right
01 01 72 73 16 15 73 71

1 rows × 8 columns

General
Filename(s) sub-01_task-audiovisual_ave.fif
MNE object type Evoked
Measurement date 1921-08-16 at 19:01:10 UTC
Participant sub-01
Experimenter mne_anonymize
Acquisition
Aggregation average of 1 epochs
Condition Grand average: 0.50 × Auditory/Left + 0.50 × Auditory/Right
Time range -0.200 – 0.499 s
Baseline -0.200 – 0.000 s
Sampling frequency 150.15 Hz
Time points 106
Channels
Magnetometers
Gradiometers
EEG
Head & sensor digitization 146 points
Filters
Highpass 0.10 Hz
Lowpass 40.00 Hz
Projections Average EEG reference (on)
meg-ECG--0.499-0.499)-PCA-01 (on)
meg-EOG--0.499-0.499)-PCA-01 (on)
eeg-EOG--0.499-0.499)-PCA-01 (on)
Time course (Magnetometers)
Time course (Gradiometers)
Time course (EEG)
Global field power
General
Filename(s) sub-01_task-audiovisual_ave.fif
MNE object type Evoked
Measurement date 1921-08-16 at 19:01:10 UTC
Participant sub-01
Experimenter mne_anonymize
Acquisition
Aggregation average of 1 epochs
Condition Grand average: 0.51 × Visual/Left + 0.49 × Visual/Right
Time range -0.200 – 0.499 s
Baseline -0.200 – 0.000 s
Sampling frequency 150.15 Hz
Time points 106
Channels
Magnetometers
Gradiometers
EEG
Head & sensor digitization 146 points
Filters
Highpass 0.10 Hz
Lowpass 40.00 Hz
Projections Average EEG reference (on)
meg-ECG--0.499-0.499)-PCA-01 (on)
meg-EOG--0.499-0.499)-PCA-01 (on)
eeg-EOG--0.499-0.499)-PCA-01 (on)
Time course (EEG)
Global field power
General
Filename(s) sub-01_task-audiovisual_ave.fif
MNE object type Evoked
Measurement date 1921-08-16 at 19:01:10 UTC
Participant sub-01
Experimenter mne_anonymize
Acquisition
Aggregation average of 1 epochs
Condition Grand average: Auditory/Left
Time range -0.200 – 0.499 s
Baseline -0.200 – 0.000 s
Sampling frequency 150.15 Hz
Time points 106
Channels
Magnetometers
Gradiometers
EEG
Head & sensor digitization 146 points
Filters
Highpass 0.10 Hz
Lowpass 40.00 Hz
Projections Average EEG reference (on)
meg-ECG--0.499-0.499)-PCA-01 (on)
meg-EOG--0.499-0.499)-PCA-01 (on)
eeg-EOG--0.499-0.499)-PCA-01 (on)
Time course (Magnetometers)
Time course (Gradiometers)
Time course (EEG)
Global field power
General
Filename(s) sub-01_task-audiovisual_ave.fif
MNE object type Evoked
Measurement date 1921-08-16 at 19:01:10 UTC
Participant sub-01
Experimenter mne_anonymize
Acquisition
Aggregation average of 1 epochs
Condition Grand average: Auditory/Right
Time range -0.200 – 0.499 s
Baseline -0.200 – 0.000 s
Sampling frequency 150.15 Hz
Time points 106
Channels
Magnetometers
Gradiometers
EEG
Head & sensor digitization 146 points
Filters
Highpass 0.10 Hz
Lowpass 40.00 Hz
Projections Average EEG reference (on)
meg-ECG--0.499-0.499)-PCA-01 (on)
meg-EOG--0.499-0.499)-PCA-01 (on)
eeg-EOG--0.499-0.499)-PCA-01 (on)
Time course (Magnetometers)
Time course (Gradiometers)
Time course (EEG)
Global field power
General
Filename(s) sub-01_task-audiovisual_ave.fif
MNE object type Evoked
Measurement date 1921-08-16 at 19:01:10 UTC
Participant sub-01
Experimenter mne_anonymize
Acquisition
Aggregation average of 1 epochs
Condition Grand average: (0.51 × Visual/Left + 0.49 × Visual/Right) - (0.50 × Auditory/Left + 0.50 × Auditory/Right)
Time range -0.200 – 0.499 s
Baseline -0.200 – 0.000 s
Sampling frequency 150.15 Hz
Time points 106
Channels
Magnetometers
Gradiometers
EEG
Head & sensor digitization 146 points
Filters
Highpass 0.10 Hz
Lowpass 40.00 Hz
Projections Average EEG reference (on)
meg-ECG--0.499-0.499)-PCA-01 (on)
meg-EOG--0.499-0.499)-PCA-01 (on)
eeg-EOG--0.499-0.499)-PCA-01 (on)
Time course (Magnetometers)
Time course (Gradiometers)
Time course (EEG)
Global field power
General
Filename(s) sub-01_task-audiovisual_ave.fif
MNE object type Evoked
Measurement date 1921-08-16 at 19:01:10 UTC
Participant sub-01
Experimenter mne_anonymize
Acquisition
Aggregation average of 1 epochs
Condition Grand average: Auditory/Right - Auditory/Left
Time range -0.200 – 0.499 s
Baseline -0.200 – 0.000 s
Sampling frequency 150.15 Hz
Time points 106
Channels
Magnetometers
Gradiometers
EEG
Head & sensor digitization 146 points
Filters
Highpass 0.10 Hz
Lowpass 40.00 Hz
Projections Average EEG reference (on)
meg-ECG--0.499-0.499)-PCA-01 (on)
meg-EOG--0.499-0.499)-PCA-01 (on)
eeg-EOG--0.499-0.499)-PCA-01 (on)
Time course (Magnetometers)
Time course (Gradiometers)
Time course (EEG)
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.
  """MNE Sample Data: M/EEG combined processing."""

import mne

bids_root = "~/mne_data/ds000248"
deriv_root = "~/mne_data/derivatives/mne-bids-pipeline/ds000248_base"
subjects_dir = f"{bids_root}/derivatives/freesurfer/subjects"

subjects = ["01"]
rename_events = {"Smiley": "Emoji", "Button": "Switch"}
conditions = ["Auditory", "Visual", "Auditory/Left", "Auditory/Right"]
epochs_metadata_query = "index > 0"  # Just for testing!
contrasts = [("Visual", "Auditory"), ("Auditory/Right", "Auditory/Left")]

time_frequency_conditions = ["Auditory", "Visual"]

ch_types = ["meg", "eeg"]
mf_reference_run = "01"
find_flat_channels_meg = True
find_noisy_channels_meg = True
use_maxwell_filter = True


def noise_cov(bp):
    """Estimate the noise covariance."""
    # Use pre-stimulus period as noise source
    bp = bp.copy().update(suffix="epo")
    if not bp.fpath.exists():
        bp.update(split="01")
    epo = mne.read_epochs(bp)
    cov = mne.compute_covariance(epo, rank="info", tmax=0)
    return cov


spatial_filter = "ssp"
n_proj_eog = dict(n_mag=1, n_grad=1, n_eeg=1)
n_proj_ecg = dict(n_mag=1, n_grad=1, n_eeg=0)
ssp_meg = "combined"
ecg_proj_from_average = True
eog_proj_from_average = False
epochs_decim = 4

bem_mri_images = "FLASH"
recreate_bem = True

n_jobs = 2


def mri_t1_path_generator(bids_path):
    """Return the path to a T1 image."""
    # don't really do any modifications – just for testing!
    return bids_path

  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