EEGdash›OpenNeuro›DS008496

Iss. 8496 · 15 subjects · 15 recordings · CC0

Dataset Brief · Brain Connectivity Based on EEG of Student Learning Processes…

DS008496: eeg dataset, 15 subjects#

Brain Connectivity Based on EEG of Student Learning Processes Using Gen-AI

Access recordings and metadata through EEGDash.

Citation: Unknown (2026). Brain Connectivity Based on EEG of Student Learning Processes Using Gen-AI. 10.18112/openneuro.ds008496.v1.0.1

Modality: eeg Subjects: 15 Recordings: 15 License: CC0 Source: openneuro

Metadata: Complete (90%)

15-participant EEG dataset — Brain Connectivity Based on EEG of Student Learning Processes Using Gen-AI.

Data & curation Authors unspecified
Year 2026 · Distributed via OpenNeuro

EEG · 60 (12), 58 (2), 59 ch256 HzBIDS 1.7.02 tasks

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#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import DS008496

dataset = DS008496(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 = DS008496(cache_dir="./data", subject="01")

Advanced query

dataset = DS008496(
    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{ds008496,
  title = {Brain Connectivity Based on EEG of Student Learning Processes Using Gen-AI},
  doi = {10.18112/openneuro.ds008496.v1.0.1},
  url = {https://doi.org/10.18112/openneuro.ds008496.v1.0.1},
}

§ 02Study · The README

About This Dataset#

This dataset a collection of electroencephalogram (EEG) data recorded from 15 subjects (8 males and 7 males) with age ranging from 20 - 22 years. This dataset is recorded from the subject while doing the task by writing the essay to assess cognitive conditions, such as creativity, memory, and critical thinking. To assess cognitive activity, participants were assigned an essay-writing task under different AI interaction conditions.

Brain Connectivity Based on EEG of Student Learning Processes Using Gen-AI

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Channel counts (ch)

585960

Sampling frequencies: 256.0 Hz (n=15 recordings)

Total recording duration: 5 h 31 min

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 60 (12), 58 (2), 59 ch · EEG · 256 Hz · 15 subjects, 15 recordings

Live trace viewer — sub-001 · task-activeai

Showing one representative recording out of 15 subjects and 15 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 · 14 sensors — 14 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

§ 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…

§ 06API · Programmatic access

API Reference#

Signature

eegdash.dataset

class

eegdash.dataset.DS008496(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)—

Canonical—

Importable asDS008496

Sourceeegdash/dataset/registry.py · [source ↗]

class eegdash.dataset.DS008496(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Brain Connectivity Based on EEG of Student Learning Processes Using Gen-AI

Study:: ds008496 (OpenNeuro)
Author (year):: —
Canonical:: —

Also importable as: DS008496.

Modality: eeg; Subject type: Unknown. Subjects: 15; recordings: 15; tasks: 2.

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/ds008496 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds008496 DOI: https://doi.org/10.18112/openneuro.ds008496.v1.0.1

Examples

>>> from eegdash.dataset import DS008496
>>> dataset = DS008496(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 FaceNo per-dataset mirror published yet — browse the EEGDash org listing for sibling datasets. See the datasets loader API.huggingface

Croissant 1.0Machine-readable JSON-LD descriptor — DS008496.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 ds008496 to reproduce the tutorial on this dataset.

Citation

Authors unspecified (2026). Brain Connectivity Based on EEG of Student Learning Processes Using Gen-AI. 10.18112/openneuro.ds008496.v1.0.1

Provenance

¹Contributed to openneuro in BIDS format.

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

³Persistent identifier: 10.18112/openneuro.ds008496.v1.0.1.

Related & sibling datasets

DS007865EEG · 24 subj DS007813EEG · 4 subj DS008446EEG · 20 subj DS008099EEG · 30 subj DS007822EEG · 33 subj

+ 1 more — see See Also below →

BIDS

BIDS 1.7.0

Sidecars

events · channels · eeg.json

Provenance

CC0 · 10.18112/openneuro.ds008496.v1.0.1

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HF org

Dataset ID	`DS008496`
Title	Brain Connectivity Based on EEG of Student Learning Processes Using Gen-AI
Author (year)	—
Canonical	—
Importable as	`DS008496`
Year	2026
Authors	Unknown
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds008496.v1.0.1
Source links	OpenNeuro \| NeMAR \| Source URL

DS008496: eeg dataset, 15 subjects#

Quickstart#

About This Dataset#

Cohort#

Dataset Statistics#

Signal · Electrodes & live trace#

NEMAR Processing Statistics#

Manifest#

File Explorer#

API Reference#

Citation

Provenance

Related & sibling datasets

See Also#