DS003702: eeg dataset, 47 subjects#

Social Memory cuing

Access recordings and metadata through EEGDash.

Citation: Samantha Gregory, Hongfang Wang, Klaus Kessler (2021). Social Memory cuing. 10.18112/openneuro.ds003702.v1.0.1

Modality: eeg Subjects: 47 Recordings: 47 License: CC0 Source: openneuro Citations: 3.0

Metadata: Complete (100%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import DS003702

dataset = DS003702(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 = DS003702(cache_dir="./data", subject="01")

Advanced query

dataset = DS003702(
    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{ds003702,
  title = {Social Memory cuing},
  author = {Samantha Gregory and Hongfang Wang and Klaus Kessler},
  doi = {10.18112/openneuro.ds003702.v1.0.1},
  url = {https://doi.org/10.18112/openneuro.ds003702.v1.0.1},
}

About This Dataset#

EEG Raw and processed data for a memory task presented in virtual reality, The full task is on OSF: https://osf.io/s9xmu/files Derviatives: Behavioral data from the task in derivatives Processed data is in the derivatives Code Event codes are listed in the code file Trial function, preprocessing, data cleaning and analysis are also in the code file. Task: Wearing a head mounted display to display the task in virtual reality participants completed a visual working memory task

View full README

EEG Raw and processed data for a memory task presented in virtual reality, The full task is on OSF: https://osf.io/s9xmu/files Derviatives: Behavioral data from the task in derivatives Processed data is in the derivatives Code Event codes are listed in the code file Trial function, preprocessing, data cleaning and analysis are also in the code file. Task: Wearing a head mounted display to display the task in virtual reality participants completed a visual working memory task In the task they had to remember the status of and details about presented objects. A person or a stick cued the items such that it could look left or right and items could appear on the left or right. Sometimes the cue was valid (i.e. pointed where objects appeared) and sometimes it was invalid (pointed away from where objects appeared) The objects were always a bowl, a cup, a plate and a teapot. Each could have a different status that needed to be remembered praticipants were probed on memory for item location and then item status Preprint to be added Event codes within the data set are as follows. Trial function is included in code folder. For the avatar cue s3021 Character shown - i.e. moment the avatar appears s3022 Objects shown - i.e. moment that the memory targets appear s3023 Maintenance interal - i.e. moment the memory objects leave the screen and the blank maintenance interval occurs s3024 Probe object shown - i.e. moment that participantis presented with location probe s3025 Resp 1 made - i.e. moment that the participants have responded to the location probe s3026 Q2 shown - i.e. moment that participants are asked a question about the status of the objects s3027 Resp 2 made - i.e. moment that the participants have responded to the status probe For the stick cue s3041 Stick shown - i.e. moment the stick appears s3042 Objects shown - i.e. moment that the memory targets appear s3043 Maintenance interal - i.e. moment the memory objects leave the screen and the blank maintenance interval occurs s3044 Probe object shown - i.e. moment that participantis presented with location probe s3045 Resp 1 made - i.e. moment that the participants have responded to the location probe s3046 Q2 shown - i.e. moment that participants are asked a question about the status of the objects s3047 Resp 2 made - i.e. moment that the participants have responded to the status probe Trial info EEG processed data 1: Main condition (MainCon)

1 = Stick Congruent 2 = Stick Incongruenct 3 = Avatar Congruent 4 = Avatar Incongruent

2: Cue condition left or right (MConCueLR): 1 = Stick Congruent Cue shifts left 2 = Stick Congruent Cue shifts right 3 = Stick Incongruent Cue shifts left 4 = Stick Incongruent Cue shifts right 5 = Avatar Congruent Cue shifts left 6 = Avatar Congruent Cue shifts right 7 = Avatar Incongruent Cue shifts left 8 = Avatar Incongruent Cue shifts right
3: Condition from experiment build (con): 1 = Congruent, cueshift L, items Left, same location 2 = Congruent, cueshift L, items Left, dif location 3 = Congruent, cueshift R, items Right, same location 4 = Congruent, cueshift R, items Right, dif location 5 = Incongruent, cueshift L, items Right, same location 6 = Incongruent, cueshift L, items Right, dif location 7 = Incongruent, cueshift R, items Left, same location 8 = Incongruent, cueshift R, items Left, dif location
4: Validity: 1 = valid (congruent) 2= invalid (incongruent)
5: Location: 1 = Same (i.e. probe at same location as when initially presented) 2 = Different (i.e. probe at different location as when initially presented)
6 Cue: 1 = Stick cue 2 = Avatar cue
7 Left or Right cue (LorR) specific to the cue shift: 1 = Left Stick 2 = Right Stick 3 = Left Avatar 4 = Right Avatar

8 Start: Sample time for start of the trial 9 Cue: Sample time for cue onset 10 Targets: Sample time for target onset 11 Maintenance: Sample time for start of mainanance interval 12 Location probe: Sample time for location probe being shown 13 Location response time: Sample time for response to location probe being made 14 Status question: Sample time for status question being asked 15 Status response time: Sample time for response to status question being made 16 Accuracy for the location question 17 Accuracy for the status question

Dataset Information#

Dataset ID	`DS003702`
Title	Social Memory cuing
Author (year)	`Gregory2021`
Canonical	—
Importable as	`DS003702`, `Gregory2021`
Year	2021
Authors	Samantha Gregory, Hongfang Wang, Klaus Kessler
License	CC0
Citation / DOI	10.18112/openneuro.ds003702.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: 47
Recordings: 47
Tasks: 1

Channels & sampling rate

Channels: 59
Sampling rate (Hz): 500.0
Duration (hours): Not calculated

Electrode Layout#

Electrode layout — EEG · 59 sensors — 59 channels

Dataset Statistics#

Age distribution (n=47, range 18–32 yr)

15202530

Sex distribution

31

16

Female Male Total: 47

Channel counts: 59 ch (n=47 recordings)

Sampling frequencies: 500.0 Hz (n=47 recordings)

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

Signal Preview#

Live trace viewer — sub-13 · task-SocialMemoryCuing

Showing one representative recording out of 47 subjects and 47 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.

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.

Files: —

Size: —

Subjects: —

Click to load file structure…

API Reference#

Use the DS003702 class to access this dataset programmatically.

class eegdash.dataset.DS003702(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

Social Memory cuing

Study:: ds003702 (OpenNeuro)
Author (year):: Gregory2021
Canonical:: —

Also importable as: DS003702, Gregory2021.

Modality: eeg. Subjects: 47; recordings: 47; 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/ds003702 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003702 DOI: https://doi.org/10.18112/openneuro.ds003702.v1.0.1 NEMAR citation count: 3

Examples

>>> from eegdash.dataset import DS003702
>>> dataset = DS003702(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)