DS006801: eeg dataset, 21 subjects#
Resting-state EEG before and after different study methods
Citation: Paloma Victoria de Sales Alves, Antonio Simeão Sobrinho Neto, Carla Alexandra da Silva Moita Minervino (—). Resting-state EEG before and after different study methods. 10.18112/openneuro.ds006801.v1.0.0
21-participant EEG dataset — Resting-state EEG before and after different study methods.
Quickstart#
Install
pip install eegdash
Access the data
from eegdash.dataset import DS006801
dataset = DS006801(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)
Filter by subject
dataset = DS006801(cache_dir="./data", subject="01")
Advanced query
dataset = DS006801(
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{ds006801,
title = {Resting-state EEG before and after different study methods},
author = {Paloma Victoria de Sales Alves and Antonio Simeão Sobrinho Neto and Carla Alexandra da Silva Moita Minervino},
doi = {10.18112/openneuro.ds006801.v1.0.0},
url = {https://doi.org/10.18112/openneuro.ds006801.v1.0.0},
}
About This Dataset#
The RECAP-EEG: Retrieval with Feedback and Cognitive Adaptation EEG Dataset provides an open-access collection of human electroencephalography (EEG) recordings aimed at investigating the neural correlates of learning processes in educational contexts. The study involved 21 neurotypical undergraduate students (mean age = 23.10 years, SD = 3.92) and was conducted at the Federal University of Paraíba (UFPB), Brazil. Participants were randomly assigned to one of three experimental groups through an automated Python v3.12.7 script that ensured continuous balance among groups. In the active learning group, participants completed a review session using the NeuroShow platform, which consisted of 10 retrieval-practice questions with immediate feedback after each response. In the passive learning group, participants performed a review session based on their own notes taken during the lecture. In the control group, participants watched the same lecture but did not perform any review activity.
Before data collection, all participants received detailed written instructions recommending that they avoid consuming caffeine or alcohol for at least 12 hours before the session, maintain a good night’s sleep, and have a proper breakfast on the morning of the experiment. Sessions were scheduled to start at 9:00 a.m., with a maximum delay tolerance of 15 minutes. Upon arrival at the laboratory, participants were briefed about the procedures specific to their group and were given the opportunity to ask questions before the experiment began. The first EEG recording (pre-intervention) was then performed, followed by the respective study condition for each group (active, passive, or control), and finally the second EEG recording (post-intervention).
EEG signals were recorded using a 32-channel ActiChamp system (Brain Products GmbH, Germany) with active silver/silver chloride (Ag/AgCl) electrodes positioned according to the international 10–20 system. Electrode impedance was kept below 15 kΩ, with the ground at Fpz. Signals were sampled at 500 Hz, filtered between 0.5 and 50 Hz, and recorded at two time points: before and immediately after the study session. Each session lasted approximately nine minutes, comprising four blocks: two eyes-open blocks (2 minutes and 15 seconds each) and two eyes-closed blocks (2 minutes and 15 seconds each). The raw EEG data are organized in compliance with the BIDS (Brain Imaging Data Structure) standard and include .vhdr, .eeg, and .vmrk files, as well as the required metadata and descriptive files. Signal quality was ensured through impedance control and power spectral density (PSD) analysis, which confirmed the integrity and consistency of the recordings.
The RECAP-EEG dataset may contribute to research in cognitive neuroscience and learning, particularly studies on retrieval practice with feedback, attentional modulation, and functional reorganization associated with active learning. It also supports interdisciplinary investigations in educational neuroscience, cognitive training, and neural modeling of learning and memory processes. The study was approved by the Research Ethics Committee of the Health Sciences Center at the Federal University of Paraíba (CCS/UFPB) under CAAE number 84958824.1.0000.5188 and approval number 7.400.264. All participants provided written informed consent prior to participation. The data are released under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction provided that proper credit is given to the original authors.
Cohort#
Dataset Statistics#
Channel counts: 31 ch (n=42 recordings)
Sampling frequencies: 500.0 Hz (n=42 recordings)
Total recording duration: 6 h 20 min
Signal · Electrodes & live trace#
Live trace viewer — sub-13 · ses-pre · task-rest
Showing one representative recording out of
21 subjects and 42 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.
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 |
Resting-state EEG before and after different study methods |
Author (year) |
|
Canonical |
— |
Importable as |
|
Year |
— |
Authors |
Paloma Victoria de Sales Alves, Antonio Simeão Sobrinho Neto, Carla Alexandra da Silva Moita Minervino |
License |
CC0 |
Citation / DOI |
|
Source links |
OpenNeuro | NeMAR | Source URL |
Copy-paste BibTeX
@dataset{ds006801,
title = {Resting-state EEG before and after different study methods},
author = {Paloma Victoria de Sales Alves and Antonio Simeão Sobrinho Neto and Carla Alexandra da Silva Moita Minervino},
doi = {10.18112/openneuro.ds006801.v1.0.0},
url = {https://doi.org/10.18112/openneuro.ds006801.v1.0.0},
}
API Reference#
eegdash.datasetEEGDashDatasetDS006801 · Alves2025eegdash/dataset/registry.py · [source ↗]- class eegdash.dataset.DS006801(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
Resting-state EEG before and after different study methods
- Study:
ds006801(OpenNeuro)- Author (year):
Alves2025- Canonical:
—
Also importable as:
DS006801,Alves2025.Modality:
eeg; Experiment type:Learning; Subject type:Healthy. Subjects: 21; recordings: 42; 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/ds006801 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006801 DOI: https://doi.org/10.18112/openneuro.ds006801.v1.0.0
Examples
>>> from eegdash.dataset import DS006801 >>> dataset = DS006801(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/ds006801").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 ds006801 to reproduce the tutorial on this dataset.
Citation
Paloma Victoria de Sales Alves, Antonio Simeão Sobrinho Neto, Carla Alexandra da Silva Moita Minervino (n.d.). Resting-state EEG before and after different study methods. 10.18112/openneuro.ds006801.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.ds006801.v1.0.0.
See Also#
eegdash.dataset.EEGDashDataseteegdash.dataset