DS004166#

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

Access recordings and metadata through EEGDash.

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) (2022). Effects of Forward and Backward Span Trainings on Working Memory: Evidence from a Randomized, Controlled Trial. 10.18112/openneuro.ds004166.v1.0.0

Modality: eeg Subjects: 71 Recordings: 431 License: CC0 Source: openneuro Citations: 1.0

Metadata: Good (80%)

Quickstart#

Get Started

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)

Query & Filter

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'])

Cite This Dataset

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},
}

About This Dataset#

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

Introduction

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

Dataset Information#

Dataset ID	`DS004166`
Title	Effects of Forward and Backward Span Trainings on Working Memory: Evidence from a Randomized, Controlled Trial
Year	2022
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

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: 71
Recordings: 431
Tasks: —

Channels & sampling rate

Channels: Varies
Sampling rate (Hz): Varies
Duration (hours): 0.0

API Reference#

Use the DS004166 class to access this dataset programmatically.

class eegdash.dataset.DS004166(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds004166. 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

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, 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