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)
Global field power
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)
Global field power
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)
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: AdvanceTempo vs. DelayTempo
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.
CSP decoding: AdvanceTempo vs. DelayTempo
Mean decoding scores. Error bars represent bootstrapped 95% confidence intervals.
CSP TF decoding: AdvanceTempo vs. DelayTempo
Found 0 clusters with p < 0.05 (clustering bins with absolute t-values > nan).
  """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-5.15.0-1077-aws-x86_64-with-glibc2.35
Python               3.12.4 (main, Jun  8 2024, 23:40:19) [GCC 11.4.0]
Executable           /home/circleci/.pyenv/versions/3.12.4/bin/python3.12
CPU                  Intel(R) Xeon(R) Platinum 8223CL CPU @ 3.00GHz (36 cores)
Memory               69.1 GiB

Core
├☑ mne               1.10.0.dev67+gbe27cf8dd (devel, latest release is 1.9.0)
├☑ numpy             2.1.3 (OpenBLAS 0.3.27 with 2 threads)
├☑ scipy             1.15.2
└☑ matplotlib        3.10.1 (backend=agg)

Numerical (optional)
├☑ sklearn           1.6.1
├☑ numba             0.61.0
├☑ nibabel           5.3.2
├☑ pandas            2.2.3
├☑ h5io              0.2.4
├☑ h5py              3.13.0
└☐ unavailable       nilearn, dipy, openmeeg, cupy

Visualization (optional)
├☑ pyvista           0.44.2 (OpenGL 4.5 (Core Profile) Mesa 23.2.1-1ubuntu3.1~22.04.3 via llvmpipe (LLVM 15.0.7, 256 bits))
├☑ pyvistaqt         0.11.2
├☑ vtk               9.3.1
├☑ qtpy              2.4.3 (PyQt6=6.8.2)
└☐ unavailable       ipympl, pyqtgraph, mne-qt-browser, ipywidgets, trame_client, trame_server, trame_vtk, trame_vuetify

Ecosystem (optional)
├☑ mne-bids          0.17.0.dev32+g3a25345
├☑ mne-bids-pipeline 1.10.0.dev68+gb995b51
├☑ edfio             0.4.6
├☑ pybv              0.7.6
└☐ unavailable       mne-nirs, mne-features, mne-connectivity, mne-icalabel, neo, eeglabio, mffpy