DS005522: ieeg dataset, 55 subjects#
Spatial Navigation Memory of Object Locations
Citation: Haydn G. Herrema, Michael J. Kahana (—). Spatial Navigation Memory of Object Locations. 10.18112/openneuro.ds005522.v1.0.0
55-participant iEEG dataset — Spatial Navigation Memory of Object Locations.
Quickstart#
Install
pip install eegdash
Access the data
from eegdash.dataset import DS005522
dataset = DS005522(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)
Filter by subject
dataset = DS005522(cache_dir="./data", subject="01")
Advanced query
dataset = DS005522(
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{ds005522,
title = {Spatial Navigation Memory of Object Locations},
author = {Haydn G. Herrema and Michael J. Kahana},
doi = {10.18112/openneuro.ds005522.v1.0.0},
url = {https://doi.org/10.18112/openneuro.ds005522.v1.0.0},
}
About This Dataset#
This dataset contains behavioral events and intracranial electrophysiological recordings from a spatial navigation memory task. The experiment consists of participants encoding object locations during a guided navigation learning phase and then recalling the object locations during a self-navigation test phase. The data was collected at clinical sites across the country as part of a collaboration with the Computational Memory Lab at the University of Pennsylvania.
Each session contains 50 trials (2 practice and 48 experimental), and each overall “trial” contains 2 learning trials followed by 1 test trial with the same object at the same location. For learning trial 1, participants are placed at a random location at a given radius from the object. They are smoothly turned to face the object (1 s), automatically driven to the object location (3 s), and then paused at the object (1 s). 5 seconds later, participants are placed at a new random location and the process repeats for learning trial 2. On test trials, participants are placed at a random location and orientation, with the object invisible. They navigate to where they believe the object was located and press a button to record their response. The environment for all sessions and trials is 64.8 x 36, with coordinates: x = (-32.4, 32.4), y = (-18.0, 18.0).
The trials are blocked by a counterbalanced scheme, so for every trial there is another trial with reflected object position, starting position, and orientation. Each block contains 2 trials (i.e., 2 x (2 learning, 1 test)), with object (X, Y) and starting locations (x, y): - (X1, Y1)
(x1’, y1’)
(x1’’, y1’’)
(x1’’’, y1’’’)
Spatial Navigation Memory of Object Locations
Description
- (X2, Y2)
(x2’, y2’)
(x2’’, y2’’)
(x2’’’, y2’’’)
The paired block contains 2 trials in the opposite order with object and starting locations: - (-X2, -Y2)
(-x2’, -y2’)
(-x2’’, -y2’’)
(-x2’’’, -y2’’’)
- (-X1, -Y1)
(-x1’, -y1’)
(-x1’’, -y1’’)
(-x1’’’, -y1’’’)
To Note
* The iEEG recordings are labeled either “monopolar” or “bipolar.” The monopolar recordings are referenced (typically a mastoid reference), but should always be re-referenced before analysis. The bipolar recordings are referenced according to a paired scheme indicated by the accompanying bipolar channels tables. * Each subject has a unique montage of electrode locations. MNI and Talairach coordinates are provided when available. * Recordings done with the Blackrock system are in units of 250 nV, while recordings done with the Medtronic system are estimated through testing to have units of 0.1 uV. We have completed the scaling to provide values in V.
Contact
For questions or inquiries, please contact sas-kahana-sysadmin@sas.upenn.edu.
Cohort#
Dataset Statistics#
Age distribution by gender (n=55, range 19–60 yr, mean 34.4 yr)
Sex composition
Channel counts (ch)
Sampling frequencies (Hz)
Total recording duration: 145 h
Signal · Electrodes & live trace#
Live trace viewer — sub-R1037D · ses-2 · task-YC1
Showing one representative recording out of
55 subjects and 176 recordings in this dataset.
Browse the full set on OpenNeuro;
drop any other _ieeg.{set,edf,bdf,vhdr} file onto the
viewer (or pass ?ieeg=<url>) to inspect it.
Electrode layout — iEEG · 188 sensors — 188 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 Navigation Memory of Object Locations |
Author (year) |
|
Canonical |
— |
Importable as |
|
Year |
— |
Authors |
Haydn G. Herrema, Michael J. Kahana |
License |
CC0 |
Citation / DOI |
|
Source links |
OpenNeuro | NeMAR | Source URL |
Copy-paste BibTeX
@dataset{ds005522,
title = {Spatial Navigation Memory of Object Locations},
author = {Haydn G. Herrema and Michael J. Kahana},
doi = {10.18112/openneuro.ds005522.v1.0.0},
url = {https://doi.org/10.18112/openneuro.ds005522.v1.0.0},
}
API Reference#
eegdash.datasetEEGDashDatasetDS005522 · Herrema2024_Spatialeegdash/dataset/registry.py · [source ↗]- class eegdash.dataset.DS005522(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
Spatial Navigation Memory of Object Locations
- Study:
ds005522(OpenNeuro)- Author (year):
Herrema2024_Spatial- Canonical:
—
Also importable as:
DS005522,Herrema2024_Spatial.Modality:
ieeg; Experiment type:Memory; Subject type:Unknown. Subjects: 55; recordings: 176; tasks: 1.- 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/ds005522 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005522 DOI: https://doi.org/10.18112/openneuro.ds005522.v1.0.0 NEMAR citation count: 0
Examples
>>> from eegdash.dataset import DS005522 >>> dataset = DS005522(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/ds005522").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 ds005522 to reproduce the tutorial on this dataset.
Citation
Haydn G. Herrema, Michael J. Kahana (n.d.). Spatial Navigation Memory of Object Locations. 10.18112/openneuro.ds005522.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.ds005522.v1.0.0.
Related & sibling datasets
+ 1 more — see See Also below →
See Also#
eegdash.dataset.EEGDashDataseteegdash.dataset