DS004117#

Sternberg Working Memory

Access recordings and metadata through EEGDash.

Citation: Julie Onton (data), Scott Makeig (data and curation), Arnaud Delorme (data and curation), Dung Truong (curation), Kay Robbins (curation) (2022). Sternberg Working Memory. 10.18112/openneuro.ds004117.v1.0.1

Modality: eeg Subjects: 23 Recordings: 601 License: CC0 Source: openneuro Citations: 2.0

Metadata: Complete (100%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import DS004117

dataset = DS004117(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 = DS004117(cache_dir="./data", subject="01")

Advanced query

dataset = DS004117(
    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{ds004117,
  title = {Sternberg Working Memory},
  author = {Julie Onton (data) and Scott Makeig (data and curation) and Arnaud Delorme (data and curation) and Dung Truong (curation) and Kay Robbins (curation)},
  doi = {10.18112/openneuro.ds004117.v1.0.1},
  url = {https://doi.org/10.18112/openneuro.ds004117.v1.0.1},
}

About This Dataset#

Modified Sternberg Working Memory Experiment

Project name: EEG and working memory

Years the project ran: 2004-05

Brief overview of experiment task: The purpose of this Modified Sternberg task study was to explore source-resolved EEG brain dynamics associated with selectively committing a series of letters to memory,

View full README

Modified Sternberg Working Memory Experiment

Project name: EEG and working memory

Years the project ran: 2004-05

Brief overview of experiment task: The purpose of this Modified Sternberg task study was to explore source-resolved EEG brain dynamics associated with selectively committing a series of letters to memory, then after a brief maintenance period responding by button press either yes or no to the question of whether a presented query letter had been in the just-presented set of to-be-memorized letters.

The task is a modified version of the classic Sternberg working memory task, with two added features: (1) interspersing the sequence of presented (black) letters to be memorized with (green) letters to be ignored, and (2) delivering auditory feedback on each trial as to the correctness of the participant response (beep = correct, buzz = incorrect).

Data collection: Scalp EEG data were collected from 71 scalp electrode channels, each referred to a right mastoid electrode, at a sampling rate of 250 Hz/channel within an analog passband of 0.1 to 100 Hz.

**Contact person: Julie Onton <julieonton@gmail.com>, ORCID#:0000-0002-5602-3557.

Access information: Contributed to OpenNeuro.org and NEMAR.org in BIDS format following annotation using HED 8.0.0 in April, 2022.

Independent variables: Letter category (to_memorize, to_ignore); numbers of presented letters to_memorize/to_ignore (3/5, 5/3, 7/1); probe letter category (in/not in the presented set). Note, only letters to be memorized appear as in set probe letters.

Dependent variables: EEG; button press response latency; participant response (correct/incorrect).

Participant pool: The dataset includes data collected from 23 healthy young adult subjects (7 male, 6 female, 11 unidentified) between the ages of 19 and 40 years of age.

Apparatus: A Neurobehavioral Systems, Inc. EEG system running under Window98 acquired the data. The experiment control program was Presentation (Neurobehavioral Systems, Inc.).

Initial setup: EEG data were collected from 71 channels (69 scalp and two periocular electrodes, all referred to right mastoid) with an analog pass band of 0.01 to 100 Hz (SA Instrumentation, San Diego). Input impedances were brought under 5 kOhms by careful scalp preparation.

Data for subjects 1-12 was acquired at a sampling rate of 250Hz. The data for subject 14 was acquired at 1000 Hz and the data for subjects 15-24 was a acquired using a 500 Hz sampling rate.

Task organization: Data was organized into runs of 25 trials each followed by a rest. Each block was a separate run in the BIDS dataset.

Task details: Each trial consisted of the following sequence of events:

[Trial initiation]. After a self-selected, variable delay, the subject initiated the next trial by pressing either response button, triggering the reappearance of the fixation cross.

[Letter sequence presentation]. In these experiments, following a 5s presentation of a central fixation cross cue, a series of 8 visual letters (~2 deg of visual angle) were presented at screen center for 1.2s followed by a 0.2s ISI:

Either 3, 5, or 7 of these were colored black.
The participant was to memorize as letters in this set.
The other 5, 3, or 1 letters in the sequence were colored green and participants were to ignore these.
The letters were drawn without substitution from the English alphabet (omitting only A, E, I, O, and U).
The presentation order of black and green letters was pseudo-random.

[Memory maintenance]. In place of a ninth letter, a dash appeared on the screen to signal the beginning of a Memory Maintenance period lasting between 2 to 4 s. During this period subjects were to silently rehearse the identities of the memorized letters.

[Memory probe]. A (red) probe letter then appeared, prompting the subject to respond by pressing one of two buttons (with the thumb or index finger of their dominant hand) to indicate whether or not the probe letter had been in the trial?s to-be-memorized letter set.

[Response feedback]. An auditory feedback signal (a confirmatory beep or cautionary buzz), then presented beginning at 400 ms after the button press, informed the subject whether their response was correct or incorrect. Note: responses in the task were largely correct.

[Session time structure]. Each task session comprised of 3 or 4 task blocks of 25 trials each separated into individual run files.

Experiment location: Swartz Center for Neural Computation (SCCN), University of California San Diego, La Jolla CA (USA).

Note 1: Results presented in Onton, J., Delorme, A. and Makeig, S., 2005. Frontal midline EEG dynamics during working memory. Neuroimage, 27(2), pp.341-356.

Note 2: This paradigm is one of 20 event-related EEG task paradigms selected for replication by the EEGManyLabs project. For details, see https://psyarxiv.com/528nr/. Contact: Yuri Pavlov <pavlovug@gmail.com>.

Note 3: Participant 5 did not have feedback events in the trials.

Note 4: The code subdirectory has several auxilliary files that were produced during the curation process. The curation was done using a series of Jupyter notebooks that are available as run in the code/curation_notebooks subdirectory.

During the running of these curation notebooks information about the status was logged using the HEDLogger. The output of the logging process is in code/curation_logs.

Updated versions of the curation notebooks can be found at: hed-standard/hed-examples

Dataset Information#

Dataset ID	`DS004117`
Title	Sternberg Working Memory
Year	2022
Authors	Julie Onton (data), Scott Makeig (data and curation), Arnaud Delorme (data and curation), Dung Truong (curation), Kay Robbins (curation)
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds004117.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: 23
Recordings: 601
Tasks: 1

Channels & sampling rate

Channels: 69 (85), 71 (85)
Sampling rate (Hz): 250.0 (94), 500.0 (48), 500.059 (22), 1000.0 (6)
Duration (hours): 0.0

API Reference#

Use the DS004117 class to access this dataset programmatically.

class eegdash.dataset.DS004117(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds004117. Modality: eeg; Experiment type: Memory; Subject type: Healthy. Subjects: 23; recordings: 85; 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/ds004117 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004117

Examples

>>> from eegdash.dataset import DS004117
>>> dataset = DS004117(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