DS005411: ieeg dataset, 47 subjects#
Free Recall of Word Lists with Repeated Items
Citation: Haydn G. Herrema, Michael J. Kahana (—). Free Recall of Word Lists with Repeated Items. 10.18112/openneuro.ds005411.v1.0.0
47-participant iEEG dataset — Free Recall of Word Lists with Repeated Items.
Quickstart#
Install
pip install eegdash
Access the data
from eegdash.dataset import DS005411
dataset = DS005411(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)
Filter by subject
dataset = DS005411(cache_dir="./data", subject="01")
Advanced query
dataset = DS005411(
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{ds005411,
title = {Free Recall of Word Lists with Repeated Items},
author = {Haydn G. Herrema and Michael J. Kahana},
doi = {10.18112/openneuro.ds005411.v1.0.0},
url = {https://doi.org/10.18112/openneuro.ds005411.v1.0.0},
}
About This Dataset#
This dataset contains behavioral events and intracranial electrophysiological recordings from a repated item free recall task. The experiment consists of participants studying a list of words, presented visually one at a time, and then freely recalling the words from the just-presented list in any order. On each list, there is a 7-second delay period between the encoding and recall phases. The data were collected at clinical sites across the country as part of a collaboration with the Computational Memory Lab at the University of Pennsylvania.
The main manipulation in this paradigm is the repetition of items in the studied list. In total, each list contains 27 encoding events, but only 12 unique words: 3 are presented one time, 3 are presented two times, and 6 are presented three times.
Free Recall of Word Lists with Repeated Items
Description
To Note
* The duration of the encoding events (i.e., length of word presentation) varies among sessions. For some sessions, the words remained on screen from 750 ms, while in other sessions presentation lasted for 1600 ms. The duration column of the events tsv files contains this information.
* 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=46, range 20–62 yr, mean 37.5 yr)
Sex composition
Channel counts (ch)
Sampling frequencies (Hz)
Total recording duration: 140 h
Signal · Electrodes & live trace#
Live trace viewer — sub-R1566D · ses-4 · task-RepFR1
Showing one representative recording out of
47 subjects and 193 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 · 168 sensors — 168 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 |
Free Recall of Word Lists with Repeated Items |
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{ds005411,
title = {Free Recall of Word Lists with Repeated Items},
author = {Haydn G. Herrema and Michael J. Kahana},
doi = {10.18112/openneuro.ds005411.v1.0.0},
url = {https://doi.org/10.18112/openneuro.ds005411.v1.0.0},
}
API Reference#
eegdash.datasetEEGDashDatasetDS005411 · Herrema2024_Freeeegdash/dataset/registry.py · [source ↗]- class eegdash.dataset.DS005411(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
Free Recall of Word Lists with Repeated Items
- Study:
ds005411(OpenNeuro)- Author (year):
Herrema2024_Free- Canonical:
—
Also importable as:
DS005411,Herrema2024_Free.Modality:
ieeg; Experiment type:Memory; Subject type:Epilepsy. Subjects: 47; recordings: 193; 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/ds005411 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005411 DOI: https://doi.org/10.18112/openneuro.ds005411.v1.0.0 NEMAR citation count: 0
Examples
>>> from eegdash.dataset import DS005411 >>> dataset = DS005411(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/ds005411").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 ds005411 to reproduce the tutorial on this dataset.
Citation
Haydn G. Herrema, Michael J. Kahana (n.d.). Free Recall of Word Lists with Repeated Items. 10.18112/openneuro.ds005411.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.ds005411.v1.0.0.
Related & sibling datasets
+ 1 more — see See Also below →
See Also#
eegdash.dataset.EEGDashDataseteegdash.dataset