DS004706: eeg dataset, 34 subjects#
Spatial memory and non-invasive closed-loop stimulus timing
Citation: Joseph H. Rudoler, Matthew R. Dougherty, Brandon S. Katerman, James P. Bruska, Woohyeuk Chang, David J. Halpern, Nicholas B. Diamond, Michael J. Kahana (20). Spatial memory and non-invasive closed-loop stimulus timing. 10.18112/openneuro.ds004706.v1.0.0
34-participant EEG dataset — Spatial memory and non-invasive closed-loop stimulus timing.
Quickstart#
Install
pip install eegdash
Access the data
from eegdash.dataset import DS004706
dataset = DS004706(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)
Filter by subject
dataset = DS004706(cache_dir="./data", subject="01")
Advanced query
dataset = DS004706(
cache_dir="./data",
query={"subject": {"$in": ["01", "02"]}},
)
Iterate recordings
for rec in dataset:
print(rec.subject, rec.raw.info['sfreq'])
If you use this dataset in your research, please cite the original authors.
BibTeX
@dataset{ds004706,
title = {Spatial memory and non-invasive closed-loop stimulus timing},
author = {Joseph H. Rudoler and Matthew R. Dougherty and Brandon S. Katerman and James P. Bruska and Woohyeuk Chang and David J. Halpern and Nicholas B. Diamond and Michael J. Kahana},
doi = {10.18112/openneuro.ds004706.v1.0.0},
url = {https://doi.org/10.18112/openneuro.ds004706.v1.0.0},
}
About This Dataset#
This dataset contains behavioral events and electrophysiological recordings from an experiment run in the Computational Memory Lab at the University of Pennsylvania from 2021-2022 with funding from U.S. Army Medical Research and Development Command (USAMRDC) through the Medical Technology Enterprise Consortium (MTEC) project MTEC-20-06-MOM-013, “Restoring memory with task-independent semi-chronic closed-loop direct brain stimulation and non-invasive closed-loop stimulus timing optimization”. This experiment constitutes the non-invasive portion of the project, which targeted memory improvement through classifier-based stimulus presentation.
The experiment is a hybrid spatial-navigation and free recall paradigm in which subjects play the role of a courier delivering items to stores across a virtual town, and are subsequently asked to recall their deliveries. There are two phases - “read-only” and “closed-loop”. In read-only sessions, there is no classifier-based timing manipulation and participants simply perform the task in order to generate training data for the models used in subsequent closed-loop sessions. After collecting sufficient training data, classifier models predict recall in closed-loop sessions and the stimulus presentation is timed to coincide with predicted good or bad memory encoding.
Two publications are based on this experiment: “Neural correlates of memory in an immersive spatiotemporal context” studies the navigation and memory dynamics in read-only sessions, and “Optimizing learning via real-time neural decoding” (link pending) explores the results of the closed-loop manipulation.
Note: memory dynamics in closed-loop sessions are potentially influenced by the closed-loop timing manipulation, and so may be biased in a way that precludes them from analyses of general mnemonic function. The read-only sessions, however, were not subject to this manipulation and therefore can be used for studying spatial and episodic memory (as in the first paper mentioned above).
Cohort#
Dataset Statistics#
Channel counts: 137 ch (n=298 recordings)
Sampling frequencies: 2048.0 Hz (n=298 recordings)
Total recording duration: 470 h
Signal · Electrodes & live trace#
Live trace viewer — sub-LTP451 · ses-3 · task-NiclsCourierClosedLoop
Showing one representative recording out of
34 subjects and 298 recordings in this dataset.
Browse the full set on OpenNeuro;
drop any other _eeg.{set,edf,bdf,vhdr} file onto the
viewer (or pass ?eeg=<url>) to inspect it.
Electrode layout — EEG · 128 sensors — 128 channels
NEMAR Processing Statistics#
The plots below are generated by NEMAR’s automated EEG pipeline. The histogram shows pipeline success for data cleaning and ICA decomposition, the percentage of data frames and EEG channels retained after artefact removal, line noise per channel (RMS, dB), and the age/gender distribution of participants.
HED event descriptors word cloud
Manifest#
File Explorer#
Browse the BIDS file structure of this dataset. Records are fetched on demand from the EEGDash catalog the first time you open the explorer.
Full dataset metadata table
Dataset ID |
|
Title |
Spatial memory and non-invasive closed-loop stimulus timing |
Author (year) |
|
Canonical |
— |
Importable as |
|
Year |
20 |
Authors |
Joseph H. Rudoler, Matthew R. Dougherty, Brandon S. Katerman, James P. Bruska, Woohyeuk Chang, David J. Halpern, Nicholas B. Diamond, Michael J. Kahana |
License |
CC0 |
Citation / DOI |
|
Source links |
OpenNeuro | NeMAR | Source URL |
Copy-paste BibTeX
@dataset{ds004706,
title = {Spatial memory and non-invasive closed-loop stimulus timing},
author = {Joseph H. Rudoler and Matthew R. Dougherty and Brandon S. Katerman and James P. Bruska and Woohyeuk Chang and David J. Halpern and Nicholas B. Diamond and Michael J. Kahana},
doi = {10.18112/openneuro.ds004706.v1.0.0},
url = {https://doi.org/10.18112/openneuro.ds004706.v1.0.0},
}
API Reference#
eegdash.datasetEEGDashDatasetDS004706 · Rudoler2023eegdash/dataset/registry.py · [source ↗]- class eegdash.dataset.DS004706(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
Spatial memory and non-invasive closed-loop stimulus timing
- Study:
ds004706(OpenNeuro)- Author (year):
Rudoler2023- Canonical:
—
Also importable as:
DS004706,Rudoler2023.Modality:
eeg; Experiment type:Memory; Subject type:Healthy. Subjects: 34; recordings: 298; tasks: 2.- Parameters:
cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key
dataset.s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to
EEGDashDataset.
- data_dir#
Local dataset cache directory (
cache_dir / dataset_id).- Type:
Path
Notes
Each item is a recording; recording-level metadata are available via
dataset.description.querysupports MongoDB-style filters on fields inALLOWED_QUERY_FIELDSand is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.References
OpenNeuro dataset: https://openneuro.org/datasets/ds004706 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004706 DOI: https://doi.org/10.18112/openneuro.ds004706.v1.0.0 NEMAR citation count: 3
Examples
>>> from eegdash.dataset import DS004706 >>> dataset = DS004706(cache_dir="./data") >>> recording = dataset[0] >>> raw = recording.load()
- __init__(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
- save(path: str, overwrite: bool = False, offset: int = 0)[source]#
Save datasets to files by creating one subdirectory for each dataset:
path/ 0/ 0-raw.fif | 0-epo.fif description.json raw_preproc_kwargs.json (if raws were preprocessed) window_kwargs.json (if this is a windowed dataset) window_preproc_kwargs.json (if windows were preprocessed) target_name.json (if target_name is not None and dataset is raw) 1/ 1-raw.fif | 1-epo.fif description.json raw_preproc_kwargs.json (if raws were preprocessed) window_kwargs.json (if this is a windowed dataset) window_preproc_kwargs.json (if windows were preprocessed) target_name.json (if target_name is not None and dataset is raw)
- Parameters:
path (str) –
- Directory in which subdirectories are created to store
-raw.fif | -epo.fif and .json files to.
overwrite (bool) – Whether to delete old subdirectories that will be saved to in this call.
offset (int) – If provided, the integer is added to the id of the dataset in the concat. This is useful in the setting of very large datasets, where one dataset has to be processed and saved at a time to account for its original position.
BaseDataset from braindecode — windowed via create_windows_from_events.braindecodeDataLoader; supports parallel workers and on-the-fly augmentations.pytorchdatasets.load_dataset("EEGDash/ds004706").huggingface
Find datasets with the EEGDash APIQuery the catalogue, filter by task or modality, list candidates.
Load one EEG recordingResolve a single record to an MNE Raw with channels and events.
EEG recording to PyTorch DataLoaderWrap braindecode windows in a DataLoader for model training.
Preprocess EEG and create windowsFilter, resample, epoch — and persist the windowed dataset.
Save and reload prepared dataCache a windowed dataset to disk and reattach it without recompute.
Download a dataset locallyPrefetch BIDS files to a local cache and validate the layout.Swap any load_dataset(...) call for ds004706 to reproduce the tutorial on this dataset.
Citation
Joseph H. Rudoler, Matthew R. Dougherty, Brandon S. Katerman, James P. Bruska, Woohyeuk Chang, … (20). Spatial memory and non-invasive closed-loop stimulus timing. 10.18112/openneuro.ds004706.v1.0.0
Provenance
¹Contributed to openneuro in BIDS format.
²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.
³Persistent identifier: 10.18112/openneuro.ds004706.v1.0.0.
See Also#
eegdash.dataset.EEGDashDataseteegdash.dataset