AudioCueWalkingStudy | |||||||
---|---|---|---|---|---|---|---|
trial_type | subject | run | AdvanceTempo | DelayTempo | PreferredCadence | UncuedWalking | |
001 | 01 | 378 | 380 | 660 | 575 |
1 rows × 6 columns
General | ||
---|---|---|
Filename(s) | sub-001_task-AudioCueWalkingStudy_ave.fif | |
MNE object type | Evoked | |
Measurement date | Unknown | |
Participant | sub-001 | |
Experimenter | Unknown | |
Acquisition | ||
Aggregation | average of 1 epochs | |
Condition | Grand average: 0.22 × AdvanceTempo/103 + 0.07 × AdvanceTempo/113 + 0.16 × AdvanceTempo/153 + 0.23 × AdvanceTempo/203 + 0.23 × AdvanceTempo/253 + 0.09 × AdvanceTempo/333 | |
Time range | -0.195 – 0.498 s | |
Baseline | -0.195 – 0.000 s | |
Sampling frequency | 102.40 Hz | |
Time points | 72 | |
Channels | ||
EEG | ||
Head & sensor digitization | Not available | |
Filters | ||
Highpass | 0.00 Hz | |
Lowpass | 40.00 Hz | |
Projections | Average EEG reference (on) |
General | ||
---|---|---|
Filename(s) | sub-001_task-AudioCueWalkingStudy_ave.fif | |
MNE object type | Evoked | |
Measurement date | Unknown | |
Participant | sub-001 | |
Experimenter | Unknown | |
Acquisition | ||
Aggregation | average of 1 epochs | |
Condition | Grand average: 0.23 × DelayTempo/109 + 0.03 × DelayTempo/119 + 0.21 × DelayTempo/159 + 0.25 × DelayTempo/209 + 0.26 × DelayTempo/259 + 0.03 × DelayTempo/999 | |
Time range | -0.195 – 0.498 s | |
Baseline | -0.195 – 0.000 s | |
Sampling frequency | 102.40 Hz | |
Time points | 72 | |
Channels | ||
EEG | ||
Head & sensor digitization | Not available | |
Filters | ||
Highpass | 0.00 Hz | |
Lowpass | 40.00 Hz | |
Projections | Average EEG reference (on) |
General | ||
---|---|---|
Filename(s) | sub-001_task-AudioCueWalkingStudy_ave.fif | |
MNE object type | Evoked | |
Measurement date | Unknown | |
Participant | sub-001 | |
Experimenter | Unknown | |
Acquisition | ||
Aggregation | average of 1 epochs | |
Condition | Grand average: (0.22 × AdvanceTempo/103 + 0.07 × AdvanceTempo/113 + 0.16 × AdvanceTempo/153 + 0.23 × AdvanceTempo/203 + 0.23 × AdvanceTempo/253 + 0.09 × AdvanceTempo/333) - (0.23 × DelayTempo/109 + 0.03 × DelayTempo/119 + 0.21 × DelayTempo/159 + 0.25 × DelayTempo/209 + 0.26 × DelayTempo/259 + 0.03 × DelayTempo/999) | |
Time range | -0.195 – 0.498 s | |
Baseline | -0.195 – 0.000 s | |
Sampling frequency | 102.40 Hz | |
Time points | 72 | |
Channels | ||
EEG | ||
Head & sensor digitization | Not available | |
Filters | ||
Highpass | 0.00 Hz | |
Lowpass | 40.00 Hz | |
Projections | Average EEG reference (on) |
"""Mobile brain body imaging (MoBI) gait adaptation experiment.
See ds001971 on OpenNeuro: https://github.com/OpenNeuroDatasets/ds001971
"""
bids_root = "~/mne_data/ds001971"
deriv_root = "~/mne_data/derivatives/mne-bids-pipeline/ds001971"
task = "AudioCueWalkingStudy"
interactive = False
ch_types = ["eeg"]
reject = {"eeg": 150e-6}
conditions = ["AdvanceTempo", "DelayTempo"]
contrasts = [("AdvanceTempo", "DelayTempo")]
subjects = ["001"]
runs = ["01"]
epochs_decim = 5 # to 100 Hz
# This is mostly for testing purposes!
decode = True
decoding_time_generalization = True
decoding_time_generalization_decim = 2
decoding_csp = True
decoding_csp_freqs = {
"beta": [13, 20, 30],
}
decoding_csp_times = [-0.2, 0.0, 0.2, 0.4]
# Just to test that MD5 works
memory_file_method = "hash"
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
├☑ edfio 0.4.2
├☑ pybv 0.7.5
└☐ unavailable mne-nirs, mne-features, mne-connectivity, mne-icalabel, neo, eeglabio, mffpy