ds001971: Gait adaptation.¶
Mobile brain body imaging (MoBI). For more information, see OpenNeuro.
Demonstrated features¶
| Feature | This example |
|---|---|
| MEG processing | ❌ |
| EEG processing | ✅ |
| Maxwell filter | ❌ |
| Frequency filter | ✅ |
| Artifact regression | ❌ |
| SSP | ❌ |
| ICA | ❌ |
| Evoked contrasts | ✅ |
| Time-by-time decoding | ✅ |
| Time-generalization decoding | ✅ |
| CSP decoding | ✅ |
| Time-frequency analysis | ❌ |
| BEM surface creation | ❌ |
| Template MRI | ❌ |
| Dask | ❌ |
Dataset source¶
This dataset was acquired from https://openneuro.org/datasets/ds001971
How to download this dataset
Run in your terminal:
Run in your terminal
openneuro-py download \
--dataset=ds001971 \
--include=sub-001/eeg/sub-001_task-AudioCueWalkingStudy_run-01_events.tsv \
--include=sub-001/eeg/sub-001_task-AudioCueWalkingStudy_run-01_eeg.set \
--include=sub-001/eeg/sub-001_task-AudioCueWalkingStudy_run-01_eeg.fdt \
--include=sub-001/eeg/sub-001_task-AudioCueWalkingStudy_run-01_eeg.json \
--include=sub-001/eeg/sub-001_task-AudioCueWalkingStudy_run-01_electrodes.tsv \
--include=sub-001/eeg/sub-001_task-AudioCueWalkingStudy_run-01_coordsystem.json \
--include=sub-001/eeg/sub-001_task-AudioCueWalkingStudy_run-01_channels.tsv
Configuration¶
Click to expand
Python
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"