DS005059#

Paired Associates Learning: Memory for Word Pairs in Cued Recall

Access recordings and metadata through EEGDash.

Citation: Haydn G. Herrema, Michael J. Kahana (2024). Paired Associates Learning: Memory for Word Pairs in Cued Recall. 10.18112/openneuro.ds005059.v1.0.6

Modality: ieeg Subjects: 72 Recordings: 2003 License: CC0 Source: openneuro Citations: 0.0

Metadata: Complete (100%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import DS005059

dataset = DS005059(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 = DS005059(cache_dir="./data", subject="01")

Advanced query

dataset = DS005059(
    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{ds005059,
  title = {Paired Associates Learning: Memory for Word Pairs in Cued Recall},
  author = {Haydn G. Herrema and Michael J. Kahana},
  doi = {10.18112/openneuro.ds005059.v1.0.6},
  url = {https://doi.org/10.18112/openneuro.ds005059.v1.0.6},
}

About This Dataset#

Paired Associates Learning of Word Pairs

Description

This dataset contains behavioral events and intracranial electrophysiological recordings from a paired associates memory task. The experiment consists of participants studying pairs of visually presented words, solving simple arithmetic problems that function as a distractor, and then completing a cued recall task. 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 25 lists of the structure: encoding, distractor, cued recall. During encoding, 6 pairs of words are presented one pair at a time. Each pair remains on screen for 4000 ms and is followed by a 1000 ms interstimulus interval. During the cued recall, one randomly chosen word from each pair is shown, and the participant is asked to vocally recall the other word from the pair. Participants have 5000 ms for each recall, and then the next cue (i.e., a word from another pair) is shown. All 6 pairs of words are tested on each list.

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, along with brain region annotations.
Recordings were made on multiple different systems, so we have done the scaling to provide all voltage values in V.

Contact

For questions or inquiries, please contact sas-kahana-sysadmin@sas.upenn.edu.

Dataset Information#

Dataset ID	`DS005059`
Title	Paired Associates Learning: Memory for Word Pairs in Cued Recall
Year	2024
Authors	Haydn G. Herrema, Michael J. Kahana
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds005059.v1.0.6
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: 72
Recordings: 2003
Tasks: 1

Channels & sampling rate

Channels: 112 (44), 126 (30), 85 (22), 110 (20), 128 (20), 88 (18), 104 (18), 100 (18), 72 (16), 186 (16), 64 (16), 102 (14), 121 (14), 116 (14), 92 (12), 142 (12), 119 (10), 94 (10), 95 (10), 97 (10), 123 (8), 96 (8), 124 (8), 140 (8), 106 (8), 68 (8), 130 (8), 86 (8), 139 (8), 120 (6), 84 (6), 188 (6), 107 (6), 87 (6), 173 (6), 117 (6), 80 (6), 55 (6), 83 (6), 108 (6), 114 (6), 74 (6), 58 (6), 115 (4), 138 (4), 141 (4), 118 (4), 73 (4), 149 (4), 111 (4), 122 (4), 90 (2), 177 (2), 99 (2), 14 (2), 53 (2), 46 (2), 76 (2), 93 (2), 67 (2), 77 (2), 60 (2), 146 (2), 16 (2), 133 (2), 52 (2), 98 (2)
Sampling rate (Hz): 1000.0 (386), 500.0 (142), 1024.0 (16), 499.7071 (12), 1600.0 (8)
Duration (hours): 0.0

API Reference#

Use the DS005059 class to access this dataset programmatically.

class eegdash.dataset.DS005059(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds005059. Modality: ieeg; Experiment type: Memory; Subject type: Unknown. Subjects: 69; recordings: 282; 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/ds005059 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005059

Examples

>>> from eegdash.dataset import DS005059
>>> dataset = DS005059(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