localizer | ||||||
---|---|---|---|---|---|---|
trial_type | subject | coherent/down | coherent/up | incoherent/down | incoherent/up | |
01 | 30 | 30 | 30 | 30 |
1 rows × 5 columns
General | ||
---|---|---|
Filename(s) | sub-01_task-localizer_ave.fif | |
MNE object type | Evoked | |
Measurement date | 1911-12-07 at 00:00:00 UTC | |
Participant | sub-01 | |
Experimenter | mne_anonymize | |
Acquisition | ||
Aggregation | average of 1 epochs | |
Condition | Grand average: 0.51 × coherent/down + 0.49 × coherent/up | |
Time range | -0.200 – 1.000 s | |
Baseline | -0.200 – 0.000 s | |
Sampling frequency | 250.00 Hz | |
Time points | 301 | |
Channels | ||
Magnetometers | ||
Gradiometers | ||
Head & sensor digitization | 38 points | |
Filters | ||
Highpass | 1.00 Hz | |
Lowpass | 40.00 Hz |
General | ||
---|---|---|
Filename(s) | sub-01_task-localizer_ave.fif | |
MNE object type | Evoked | |
Measurement date | 1911-12-07 at 00:00:00 UTC | |
Participant | sub-01 | |
Experimenter | mne_anonymize | |
Acquisition | ||
Aggregation | average of 1 epochs | |
Condition | Grand average: 0.54 × incoherent/down + 0.46 × incoherent/up | |
Time range | -0.200 – 1.000 s | |
Baseline | -0.200 – 0.000 s | |
Sampling frequency | 250.00 Hz | |
Time points | 301 | |
Channels | ||
Magnetometers | ||
Gradiometers | ||
Head & sensor digitization | 38 points | |
Filters | ||
Highpass | 1.00 Hz | |
Lowpass | 40.00 Hz |
General | ||
---|---|---|
Filename(s) | sub-01_task-localizer_ave.fif | |
MNE object type | Evoked | |
Measurement date | 1911-12-07 at 00:00:00 UTC | |
Participant | sub-01 | |
Experimenter | mne_anonymize | |
Acquisition | ||
Aggregation | average of 1 epochs | |
Condition | Grand average: (0.54 × incoherent/down + 0.46 × incoherent/up) - (0.51 × coherent/down + 0.49 × coherent/up) | |
Time range | -0.200 – 1.000 s | |
Baseline | -0.200 – 0.000 s | |
Sampling frequency | 250.00 Hz | |
Time points | 301 | |
Channels | ||
Magnetometers | ||
Gradiometers | ||
Head & sensor digitization | 38 points | |
Filters | ||
Highpass | 1.00 Hz | |
Lowpass | 40.00 Hz |
"""hMT+ Localizer."""
bids_root = "~/mne_data/ds003392"
deriv_root = "~/mne_data/derivatives/mne-bids-pipeline/ds003392"
subjects = ["01"]
task = "localizer"
# usually a good idea to use True, but we know no bads are detected for this dataset
find_flat_channels_meg = False
find_noisy_channels_meg = False
use_maxwell_filter = True
ch_types = ["meg"]
l_freq = 1.0
h_freq = 40.0
raw_resample_sfreq = 250
crop_runs = (0, 180)
# Artifact correction.
spatial_filter = "ica"
ica_algorithm = "picard-extended_infomax"
ica_max_iterations = 1000
ica_l_freq = 1.0
ica_n_components = 0.99
# Epochs
epochs_tmin = -0.2
epochs_tmax = 1.0
baseline = (None, 0)
# Conditions / events to consider when epoching
conditions = ["coherent", "incoherent"]
# Decoding
decode = True
decoding_time_generalization = True
decoding_time_generalization_decim = 4
contrasts = [("incoherent", "coherent")]
decoding_csp = True
decoding_csp_times = []
decoding_csp_freqs = {
"alpha": (8, 12),
}
# Noise estimation
noise_cov = "emptyroom"
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