| 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) | |
"""ds001971: Gait adaptation.
Mobile brain body imaging (MoBI).
For more information, see [OpenNeuro](https://openneuro.org/datasets/ds001971).
"""
from mne_bids_pipeline.typing import ArbitraryContrast
bids_root = "~/mne_data/ds001971"
deriv_root = "~/mne_data/derivatives/mne-bids-pipeline/ds001971"
ignore_warnings = [
"Unknown types found", # ANKLE, HIP, KNEE
"Not setting positions of 4 emg channels", # TIBR1, TIBR2, TIBL1, TIBL2
'MNE mapping found for channel type "AUX"', # HIP
'"ARS" is not a BIDS-acceptable coordinate frame for EEG',
"Unable to map the following column", # handedness
"low-pass frequency of 40.0 Hz. The decim=5 parameter", # minor aliasing risk
]
task = "AudioCueWalkingStudy"
interactive = False
ch_types = ["eeg"]
reject = {"eeg": 150e-6}
conditions = ["AdvanceTempo", "DelayTempo"]
contrasts = [
ArbitraryContrast(
name="AdvanceMinusDelay",
conditions=["AdvanceTempo", "DelayTempo"],
weights=[1.0, -1.0],
),
]
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 = [-0.19, 0.0, 0.2, 0.4]
# Just to test that MD5 works
memory_file_method = "hash"
Platform Linux-7.0.0-1004-aws-x86_64-with-glibc2.43 (X11)
Python 3.14.4 (main, May 12 2026, 13:57:53) [GCC 15.2.0]
Executable /home/circleci/python_env/bin/python3
CPU AMD EPYC 9R45 (2 cores)
Memory 7.6 GiB
Core
├☑ mne 1.14.0.dev112+g8f498626a (development, latest release is 1.13.2)
├☑ numpy 2.5.4 (OpenBLAS 0.3.31.dev with 2 threads via pthreads)
├☑ scipy 1.18.1
└☑ matplotlib 3.11.2 (backend=agg)
Numerical (optional)
├☑ scikit-learn 1.9.1
├☑ threadpoolctl 3.7.0
├☑ numba 0.68.0
├☑ nibabel 5.4.2
├☑ pandas 3.0.6
├☑ h5io 0.2.5
├☑ h5py 3.16.0
└☐ unavailable nilearn, dipy, openmeeg, python-picard, jamica, cupy
Visualization (optional)
├☑ pyvista 0.49.1 (OpenGL 4.5 (Core Profile) Mesa 26.0.8-1ubuntu0.3 via llvmpipe (LLVM 21.1.8, 256 bits))
├☑ pyvistaqt 0.13.1
├☑ vtk 9.7.1
├☑ qtpy 2.4.3 (PySide6=6.12.0)
└☐ unavailable ipympl, pyqtgraph, mne-qt-browser, ipywidgets, trame, trame_client, trame_server, trame_pyvista, trame_vtk, trame_vuetify
Ecosystem (optional)
├☑ mne-bids 0.21.0.dev10+ga582be8f8
├☑ mne-icalabel 0.10.0
├☑ mne-bids-pipeline 1.11.0.dev86+ge2a34cf44
├☑ autoreject 0.6.0.dev2+g2f72b264
├☑ eeglabio 0.1.4
├☑ edfio 0.4.18
├☑ curryreader 0.1.2
├☑ mffpy 0.11.0
├☑ pybv 0.8.1
├☑ defusedxml 0.7.1
└☐ unavailable mne-nirs, mne-features, mne-connectivity, neo, pymef, antio