DS005095#

STERNBERG DIFFICULT

Access recordings and metadata through EEGDash.

Citation: Natalia Zhozhikashvili, Maria Protopova, Tatiana Shkurenko, Marie Arsalidou, Ilya Zakharov, Boris Kotchoubey, Sergey Malykh, Yuri Pavlov (2024). STERNBERG DIFFICULT. 10.18112/openneuro.ds005095.v1.0.1

Modality: eeg Subjects: 48 Recordings: 485 License: CC0 Source: openneuro Citations: 7.0

Metadata: Complete (100%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import DS005095

dataset = DS005095(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 = DS005095(cache_dir="./data", subject="01")

Advanced query

dataset = DS005095(
    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{ds005095,
  title = {STERNBERG DIFFICULT},
  author = {Natalia Zhozhikashvili and Maria Protopova and Tatiana Shkurenko and Marie Arsalidou and Ilya Zakharov and Boris Kotchoubey and Sergey Malykh and Yuri Pavlov},
  doi = {10.18112/openneuro.ds005095.v1.0.1},
  url = {https://doi.org/10.18112/openneuro.ds005095.v1.0.1},
}

About This Dataset#

Overview

This is the “Sternberg Difficult” dataset contain RAW EEG data. Raven Progressive Standard Matrices scores for each participant are provided in participants.tsv

Task

Participants completed a version of the Sternberg task (Sternberg, 1966; Fig 1) during EEG recording. Stimuli were all consonants of the Russian alphabet letters, except for “щ”[sch] and “й” [ij], presented in sets of 3, 6, 9, 12, and 15 letters. No letter repeated within a set. Each trial was preceded by a 500-1000 ms fixation cross. Encoding (letter set), retention (blank screen), and retrieval (probe letter) phases were allocated 1500ms, 2000ms and 1500ms, respectively. After 1500 ms period, the probe letter disappeared from the screen. Participants were asked to recall whether the probe letter was in the letter set presented during encoding phase. They had unlimited time to respond by pressing a button: the “left arrow” for “no” and the “right arrow” for “yes”. The trial concluded immediately after a response was made, regardless of the reaction time. Participants completed 200 trials in total with 40 trials in difficulty blocks corresponding to each particular set size (i.e., 3, 6, 9, 12, and 15 letters) with an opportunity to take a break after each block. The order of blocks was random, and the number of positive and negative probes was equal in each block. All stimuli were presented and responses were recorded using Psychopy2.

Event triggers

Important: Triggers in the dataset correspond only to the beginning of the stimulus presentation. No additional triggers were implemented to mark the onset of the retention and retrieval periods. However, these timepoints can be computed based on the experimental design. Each sample was presented for 1500 ms, meaning that the retention time occurred strictly 1500 ms after the trigger point appeared in the data. Similarly, the time of retrieval (when participants had to explicitly state whether a new letter had been shown previously) could be marked at 3500 ms relative to the trial onset.

Dataset Information#

Dataset ID	`DS005095`
Title	STERNBERG DIFFICULT
Year	2024
Authors	Natalia Zhozhikashvili, Maria Protopova, Tatiana Shkurenko, Marie Arsalidou, Ilya Zakharov, Boris Kotchoubey, Sergey Malykh, Yuri Pavlov
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds005095.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: 485
Tasks: 1

Channels & sampling rate

Channels: 63
Sampling rate (Hz): 1000.0
Duration (hours): 0.0

API Reference#

Use the DS005095 class to access this dataset programmatically.

class eegdash.dataset.DS005095(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds005095. Modality: eeg; Experiment type: Memory; Subject type: Healthy. Subjects: 48; recordings: 48; 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/ds005095 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005095

Examples

>>> from eegdash.dataset import DS005095
>>> dataset = DS005095(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