DS005411#

Free Recall of Word Lists with Repeated Items

Access recordings and metadata through EEGDash.

Citation: Haydn G. Herrema, Michael J. Kahana (2024). Free Recall of Word Lists with Repeated Items. 10.18112/openneuro.ds005411.v1.0.1

Modality: ieeg Subjects: 48 Recordings: 1370 License: CC0 Source: openneuro Citations: 0.0

Metadata: Complete (100%)

Quickstart#

Get Started

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)

Query & Filter

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'])

Cite This Dataset

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.v1.0.1},
  url = {https://doi.org/10.18112/openneuro.ds005411.v1.0.1},
}

About This Dataset#

Free Recall of Word Lists with Repeated Items

Description

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.

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.

Dataset Information#

Dataset ID	`DS005411`
Title	Free Recall of Word Lists with Repeated Items
Year	2024
Authors	Haydn G. Herrema, Michael J. Kahana
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds005411.v1.0.1
Source links	OpenNeuro \| NeMAR \| Source URL

Found an issue with this dataset?

If you encounter any problems with this dataset (missing files, incorrect metadata, loading errors, etc.), please let us know!

Report an Issue on GitHub

Technical Details#

Subjects & recordings

Subjects: 48
Recordings: 1370
Tasks: 1

Channels & sampling rate

Channels: 120 (24), 118 (20), 109 (18), 153 (14), 168 (14), 126 (12), 116 (12), 122 (12), 110 (12), 106 (10), 182 (10), 167 (8), 200 (8), 108 (8), 211 (8), 169 (8), 115 (8), 192 (8), 152 (8), 155 (8), 134 (8), 105 (6), 132 (6), 133 (6), 121 (6), 141 (6), 127 (6), 99 (4), 187 (4), 213 (4), 186 (4), 55 (4), 44 (4), 166 (4), 184 (4), 84 (4), 140 (4), 195 (4), 94 (4), 160 (4), 96 (4), 181 (4), 173 (4), 236 (2), 111 (2), 138 (2), 159 (2), 107 (2), 230 (2), 98 (2), 154 (2), 129 (2), 215 (2), 119 (2), 142 (2), 104 (2), 239 (2), 218 (2), 165 (2), 176 (2), 232 (2), 101 (2), 128 (2), 210 (2), 202 (2), 158 (2), 123 (2)
Sampling rate (Hz): 1000.0 (300), 2048.0 (42), 2000.0 (20), 512.0 (16), 1024.0 (8)
Duration (hours): 0.0

API Reference#

Use the DS005411 class to access this dataset programmatically.

class eegdash.dataset.DS005411(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds005411. 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

query#

Merged query with the dataset filter applied.

Type:: dict

records#

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and 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

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, overwrite=False)[source]#

Save the dataset to disk.

Parameters:

path (str or Path) – Destination file path.
overwrite (bool, default False) – If True, overwrite existing file.

Return type:

None