EEGdash›OpenNeuro›DS004166
Iss. 4166 · 71 subjects · 213 recordings · CC0
Dataset Brief · Effects of Forward and Backward Span Trainings on Working Memory

DS004166: eeg dataset, 71 subjects#

Effects of Forward and Backward Span Trainings on Working Memory: Evidence from a Randomized, Controlled Trial

Citation: Yang Li (data and curation), Wenjin Fu (data), Qiumei Zhang (data), Xiongying Chen (data), Xiaohong Li (data), Boqi Du (data), Xiaoxiang Deng (data), Feng Ji (curation), Qi Dong (curation), Feng Ji (curation), Susanne M. Jaeggi (curation), Chuansheng Chen (curation), Jun Li (data and curation) (—). Effects of Forward and Backward Span Trainings on Working Memory: Evidence from a Randomized, Controlled Trial. 10.18112/openneuro.ds004166.v1.0.0

71-participant EEG dataset — Effects of Forward and Backward Span Trainings on Working Memory: Evidence from a Randomized, Controlled Trial.

Data & curation Yang Li (data and curation) · Wenjin Fu (data) · Qiumei Zhang (data) · Xiongying Chen (data) · Xiaohong Li (data) · Boqi Du (data) · …
Funding This work was supported by grants from the National Natural Science Foundation of China (31771242). The authors declared that they have no conflict of interest.
BIDS 1.7.0Task · WM3 sessionsHealthyVisualLearning
Layer 01Study
What was asked
Hypothesis, independent & dependent variables, paradigm, cohort, and the editorial caveats around what the recordings can and cannot answer.
Layer 02Signal · BIDS
What was recorded
Sidecars, channels & electrodes, coordinate system, event semantics, and quality stats from the NEMAR pipeline when available.
Layer 03Training · ML
What you can train on
Recommended access modes — MNE Raw, braindecode windows, PyTorch DataLoader — plus the targets the metadata makes addressable.
§ 01Access · Get started

Quickstart#

Install

pip install eegdash

Access the data

from eegdash.dataset import DS004166

dataset = DS004166(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Filter by subject

dataset = DS004166(cache_dir="./data", subject="01")

Advanced query

dataset = DS004166(
    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{ds004166,
  title = {Effects of Forward and Backward Span Trainings on Working Memory: Evidence from a Randomized, Controlled Trial},
  author = {Yang Li (data and curation) and Wenjin Fu (data) and Qiumei Zhang (data) and Xiongying Chen (data) and Xiaohong Li (data) and Boqi Du (data) and Xiaoxiang Deng (data) and Feng Ji (curation) and Qi Dong (curation) and Feng Ji (curation) and Susanne M. Jaeggi (curation) and Chuansheng Chen (curation) and Jun Li (data and curation)},
  doi = {10.18112/openneuro.ds004166.v1.0.0},
  url = {https://doi.org/10.18112/openneuro.ds004166.v1.0.0},
}
§ 02Study · The README

About This Dataset#

Overview: Both forward and backward working memory span tasks have been used in cognitive training, but no study has

been conducted to test whether the two types of trainings are equally effective. Based on data from a larger randomized controlled trial, this study tested the effects of backward span training, forward span training, and no intervention.

Event-related potential (ERP) signals were recorded at the pre-, mid-, and post-tests while the subjects were performing

a distractor version of the change detection task, which included three conditions (2 targets and 0 distractor [2T0D];

4 targets and 0 distractor [4T0D]; and 2 targets and 2 distractors [2T2D]). Behavioral data were collected from two additional tasks: a multi-object version of the change detection task, and a suppress task. Compared to no intervention, both forward

Effects of Forward and Backward Span Trainings on Working Memory: Evidence from a Randomized, Controlled Trial

Introduction

and backward span trainings led to significantly greater improvement in working memory maintenance, based on indices from both behavioral (Kmax) and ERP data (CDA_2T0D and CDA_4T0D). Backward span training also improved interference control based on the ERP data (CDA_filtering efficiency) to a greater extent than did forward span training and no intervention, but the three groups did not differ in terms of behavioral indices of interference control. These results have potential implications for optimizing the current cognitive training on working memory.

Methods

Subjects: Volunteers from university recruited through advertisements. Apparatus: At all three time points (pre-, mid-, and post-tests), we used a 64-channel Synamps RT system (Neuroscan, El Paso, USA) to record the electroencephalogram (EEG) signals. Subjects were required to sit in a comfortable chair inside a darkened, electrically shielded recording chamber during the EEG recording. The electrode impedance was low (below 5kΩ). The reference electrode was on the left mastoid.

Electrodes were set both below and above the right eye to record the vertical electrooculographies (EOGs). Electrodes were set at the outer canthi

of each eye to record the horizontal EOGs.

EEG dataset: Backward group (sub-01~sub020); Forward group (sub-101~sub120); Control group (sub-201~sub220); Sudoku group (sub-301~sub320).

Pre-test(ses-01); Mid-test(ses-01); Post-test(ses-01);

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Age distribution by gender (n=60, range 18–27 yr, mean 21.7 yr)

152025
Female · 45Male · 15

Sex composition

60
subjects
Female
45
Male
15
F : M ratio
3.00 : 1
75% female · n = 60 subjects with reported sex.
§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage — ch · EEG · Varies · 71 subjects, 213 recordings
Live trace viewer — sub-213 · ses-02 · task-WM · run-1

Showing one representative recording out of 71 subjects and 213 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.

No scalp electrode layout is currently indexed for this dataset. Once the eegdash montage registry ingests it, the interactive viewer will appear here automatically.

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.

NEMAR pipeline statistics — DS004166
HED event descriptors word cloud HED event descriptors word cloud — DS004166
§ 05Manifest · BIDS tree

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.

Recordings—
Files—
Subjects—
Modalities—
Click to load file structure…
Full dataset metadata table

Dataset ID

DS004166

Title

Effects of Forward and Backward Span Trainings on Working Memory: Evidence from a Randomized, Controlled Trial

Author (year)

Li2022

Canonical

—

Importable as

DS004166, Li2022

Year

—

Authors

Yang Li (data and curation), Wenjin Fu (data), Qiumei Zhang (data), Xiongying Chen (data), Xiaohong Li (data), Boqi Du (data), Xiaoxiang Deng (data), Feng Ji (curation), Qi Dong (curation), Feng Ji (curation), Susanne M. Jaeggi (curation), Chuansheng Chen (curation), Jun Li (data and curation)

License

CC0

Citation / DOI

doi:10.18112/openneuro.ds004166.v1.0.0

Source links

OpenNeuro | NeMAR | Source URL
Copy-paste BibTeX
@dataset{ds004166,
  title = {Effects of Forward and Backward Span Trainings on Working Memory: Evidence from a Randomized, Controlled Trial},
  author = {Yang Li (data and curation) and Wenjin Fu (data) and Qiumei Zhang (data) and Xiongying Chen (data) and Xiaohong Li (data) and Boqi Du (data) and Xiaoxiang Deng (data) and Feng Ji (curation) and Qi Dong (curation) and Feng Ji (curation) and Susanne M. Jaeggi (curation) and Chuansheng Chen (curation) and Jun Li (data and curation)},
  doi = {10.18112/openneuro.ds004166.v1.0.0},
  url = {https://doi.org/10.18112/openneuro.ds004166.v1.0.0},
}
§ 06API · Programmatic access

API Reference#

Signature
eegdash.dataset
class
eegdash.dataset.DS004166(cache_dir, query=None, s3_bucket=None, **kwargs)
Bases: EEGDashDataset
Author (year)Li2022
Canonical—
Importable asDS004166 · Li2022
Sourceeegdash/dataset/registry.py · [source ↗]
class eegdash.dataset.DS004166(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Effects of Forward and Backward Span Trainings on Working Memory: Evidence from a Randomized, Controlled Trial

Study:

ds004166 (OpenNeuro)

Author (year):

Li2022

Canonical:

—

Also importable as: DS004166, Li2022.

Modality: eeg; Experiment type: Learning; Subject type: Healthy. Subjects: 71; recordings: 213; 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/ds004166 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004166 DOI: https://doi.org/10.18112/openneuro.ds004166.v1.0.0 NEMAR citation count: 1

Examples

>>> from eegdash.dataset import DS004166
>>> dataset = DS004166(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.

Access modesMNE → braindecode → PyTorch → ML
.rawMNE Raw object — standard tools (filter, epoch, ICA, plot_psd).mne
BaseConcatDatasetEach record is a lazy BaseDataset from braindecode — windowed via create_windows_from_events.braindecode
DataLoaderWraps the windowed dataset into a PyTorch DataLoader; supports parallel workers and on-the-fly augmentations.pytorch
Zarr cacheOptional braindecode Zarr mirror for fast resume; persisted to cache_dir.zarr
Hugging FacePre-bundled mirror at EEGDash/ds004166 · pull with datasets.load_dataset("EEGDash/ds004166").huggingface
Croissant 1.0Machine-readable JSON-LD descriptor — DS004166.croissant.json (MLCommons schema, ingestible by PyTorch / TensorFlow / JAX).mlcommons
Examples using EEGDashcurated · start here
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 ds004166 to reproduce the tutorial on this dataset.

Citation

Yang Li (data and curation), Wenjin Fu (data), Qiumei Zhang (data), Xiongying Chen (data), Xiaohong Li (data), … (n.d.). Effects of Forward and Backward Span Trainings on Working Memory: Evidence from a Randomized, Controlled Trial. 10.18112/openneuro.ds004166.v1.0.0

Provenance

¹Contributed to openneuro in BIDS format.

²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.

³Persistent identifier: 10.18112/openneuro.ds004166.v1.0.0.

BIDS
BIDS 1.7.0
Sidecars
not yet probed
Machine-readable
schema.org/Dataset · Croissant

See Also#