DS005095: eeg dataset, 48 subjects#
STERNBERG DIFFICULT
Citation: Natalia Zhozhikashvili, Maria Protopova, Tatiana Shkurenko, Marie Arsalidou, Ilya Zakharov, Boris Kotchoubey, Sergey Malykh, Yuri Pavlov (19). STERNBERG DIFFICULT. 10.18112/openneuro.ds005095.v1.0.2
48-participant EEG dataset — STERNBERG DIFFICULT.
Quickstart#
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)
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'])
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.v1.0.2},
url = {https://doi.org/10.18112/openneuro.ds005095.v1.0.2},
}
About This Dataset#
This is the “Sternberg Difficult” dataset contain RAW EEG data. Raven Progressive Standard Matrices scores for each participant are provided in participants.tsv
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.
Overview
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.
Cohort#
Dataset Statistics#
Age distribution by gender (n=48, range 19–32 yr, mean 21.1 yr)
Sex composition
Channel counts: 63 ch (n=48 recordings)
Sampling frequencies: 1000.0 Hz (n=48 recordings)
Total recording duration: 16 h 54 min
Signal · Electrodes & live trace#
Live trace viewer — sub-13 · ses-01 · task-STERNBERG
Showing one representative recording out of
48 subjects and 48 recordings in this dataset.
Browse the full set on OpenNeuro;
drop any other _eeg.{set,edf,bdf,vhdr} file onto the
viewer (or pass ?eeg=<url>) to inspect it.
Electrode layout — EEG · 63 sensors — 63 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 |
STERNBERG DIFFICULT |
Author (year) |
|
Canonical |
— |
Importable as |
|
Year |
19 |
Authors |
Natalia Zhozhikashvili, Maria Protopova, Tatiana Shkurenko, Marie Arsalidou, Ilya Zakharov, Boris Kotchoubey, Sergey Malykh, Yuri Pavlov |
License |
CC0 |
Citation / DOI |
|
Source links |
OpenNeuro | NeMAR | Source URL |
Copy-paste 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.v1.0.2},
url = {https://doi.org/10.18112/openneuro.ds005095.v1.0.2},
}
API Reference#
eegdash.datasetEEGDashDatasetDS005095 · Zhozhikashvili2024eegdash/dataset/registry.py · [source ↗]- class eegdash.dataset.DS005095(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
STERNBERG DIFFICULT
- Study:
ds005095(OpenNeuro)- Author (year):
Zhozhikashvili2024- Canonical:
—
Also importable as:
DS005095,Zhozhikashvili2024.Modality:
eeg. 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
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/ds005095 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005095 DOI: https://doi.org/10.18112/openneuro.ds005095.v1.0.2 NEMAR citation count: 7
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: 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/ds005095").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 ds005095 to reproduce the tutorial on this dataset.
Citation
Natalia Zhozhikashvili, Maria Protopova, Tatiana Shkurenko, Marie Arsalidou, Ilya Zakharov, … (19). STERNBERG DIFFICULT. 10.18112/openneuro.ds005095.v1.0.2
Provenance
¹Contributed to openneuro in BIDS format.
²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.
³Persistent identifier: 10.18112/openneuro.ds005095.v1.0.2.
See Also#
eegdash.dataset.EEGDashDataseteegdash.dataset