EEGdash›NeMAR›ON007058

Iss. 7058 · 10 subjects · 395 recordings · CC0

Dataset Brief · Silent Visual Reading EEG

ON007058: eeg dataset, 10 subjects#

Silent Visual Reading EEG

Access recordings and metadata through EEGDash.

Citation: Jiawei Li, Adrien Doerig, Radoslaw Martin Cichy (2019). Silent Visual Reading EEG. 10.82901/nemar.on007058

Modality: eeg Subjects: 10 Recordings: 395 License: CC0 Source: nemar

Metadata: Complete (100%)

10-participant EEG dataset — Silent Visual Reading EEG.

Data & curation Jiawei Li · Adrien Doerig · Radoslaw Martin Cichy
Senior author Radoslaw Martin Cichy
Year 2019 · Distributed via NeMAR

EEG · 63 ch200 HzBIDS 1.7.0Task · read7 sessions

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 ON007058

dataset = ON007058(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 = ON007058(cache_dir="./data", subject="01")

Advanced query

dataset = ON007058(
    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{on007058,
  title = {Silent Visual Reading EEG},
  author = {Jiawei Li and Adrien Doerig and Radoslaw Martin Cichy},
  doi = {10.82901/nemar.on007058},
  url = {https://doi.org/10.82901/nemar.on007058},
}

§ 02Study · The README

About This Dataset#

This dataset contains the raw and processed EEG data accompanying the paper.

The dataset includes raw EEG recordings in BrainVision format:

* .eeg * .vhdr * .vmrk

DOI

EEG Dataset for “Auditory representations of words during silent visual reading”

All participants’ data follow the BIDS (Brain Imaging Data Structure) specification.

Event Annotations

Each run includes an events file with onsets, durations, trial types, and event values for all trials.

View full README

DOI

EEG Dataset for “Auditory representations of words during silent visual reading”

All participants’ data follow the BIDS (Brain Imaging Data Structure) specification.

Event Annotations

Each run includes an events file with onsets, durations, trial types, and event values for all trials.

Stimulus Presentation

Participants viewed word stimuli forming naturalistic narratives, presented on a grey background with a central fixation cross.

Trigger | Type |

  | :--- | :---

``S111`` | Onset of a word in a unique story
``S71`` | Onset of a word in a repeated story

Additional Triggers

*\*Run onset:* S71

* Run end: S78

Derivatives

Processed data files are stored in the ./derivatives folder.

This folder contains the processed EEG, as well as the accompanying meta-data and feature embeddings.

MetaData

The following files are found in the ./derivatives/MetaData/ sub-folder:

Story–Run–Session Match Table

Path: ./derivatives/MetaData/session_story_run Description: A table matching each recording session to the specific story and run it contains.

Story Indexes for Each Epoch

Path: ./derivatives/MetaData/story_epoch_match Description: Provides the specific story index corresponding to each epoch in the processed EEG data.

Expected Index for Each Epoch

Path: ./derivatives/MetaData/expect_or_not Description: Indicates whether the epoch was expected or unexpected. Values: - 1 = expected - 0 = unexpected

Processed EEG Data

The preprocessed EEG signals are located in: ./derivatives/eeg_processed/ Data are organized by channel, where each channel file has the following specifications: - Shape: (nWords, nTimepoints) - nWords: number of words (epochs) - nTimepoints: number of time points per epoch

Corresponding Feature Embeddings

Feature (embeddings) are stored in: ./derivatives/Features/ These feature files follow the same word-level indexing (nWords) as the EEG data: - Shape: (nWords,)

- nWords: number of words (epochs) in the EEG data

References

* Appelhoff, S., Sanderson, M., Brooks, T., Vliet, M., Quentin, R., Holdgraf, C., Chaumon, M., Mikulan, E., Tavabi, K., Höchenberger, R., Welke, D., Brunner, C., Rockhill, A., Larson, E., Gramfort, A., & Jas, M. (2019). **MNE-BIDS: Organizing electrophysiological data into the BIDS format and facilitating their analysis.**\*Journal of Open Source Software, 4*(1896). https://doi.org/10.21105/joss.01896 * Pernet, C. R., Appelhoff, S., Gorgolewski, K. J., Flandin, G., Phillips, C., Delorme, A., & Oostenveld, R. (2019). **EEG-BIDS, an extension to the brain imaging data structure for electroencephalography.**\*Scientific Data, 6*, 103. https://doi.org/10.1038/s41597-019-0104-8

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Age distribution by gender (n=10, range 20–31 yr, mean 26.6 yr)

202530

Female · 7Male · 3

Sex composition

Female
7
Male
3
F : M ratio
2.33 : 1

70% female · n = 10 subjects with reported sex.

HandednessRight · 10

Channel counts: 63 ch (n=395 recordings)

Sampling frequencies: 200.0 Hz (n=395 recordings)

Total recording duration: 37 h

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 63 ch · EEG · 200 Hz · 10 subjects, 395 recordings

Live trace viewer — sub-1010 · ses-04 · task-read · run-04

Showing one representative recording out of 10 subjects and 395 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 · 63 sensors — 63 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.ON007058(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)—

Canonical—

Importable asON007058

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

class eegdash.dataset.ON007058(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Silent Visual Reading EEG

Study:: on007058 (NeMAR)
Author (year):: —
Canonical:: —

Also importable as: ON007058.

Modality: eeg; Subject type: Unknown. Subjects: 10; recordings: 395; 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/on007058 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=on007058 DOI: https://doi.org/10.82901/nemar.on007058

Examples

>>> from eegdash.dataset import ON007058
>>> dataset = ON007058(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 — ON007058.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 on007058 to reproduce the tutorial on this dataset.

Citation

Jiawei Li, Adrien Doerig, Radoslaw Martin Cichy (2019). Silent Visual Reading EEG. 10.82901/nemar.on007058

Provenance

¹Contributed to nemar in BIDS format.

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

³Persistent identifier: 10.82901/nemar.on007058.

Related & sibling datasets

NM000269EEG · 13 subj ON007720EEG · 5 subj ON003190EEG · 19 subj ON005262EEG · 12 subj ON005170EEG · 5 subj

+ 1 more — see See Also below →

BIDS

BIDS 1.7.0

Sidecars

events · events.json · channels · eeg.json

Provenance

CC0 · 10.82901/nemar.on007058

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HF org

Dataset ID	`ON007058`
Title	Silent Visual Reading EEG
Author (year)	—
Canonical	—
Importable as	`ON007058`
Year	2019
Authors	Jiawei Li, Adrien Doerig, Radoslaw Martin Cichy
License	CC0
Citation / DOI	10.82901/nemar.on007058
Source links	OpenNeuro \| NeMAR \| Source URL

ON007058: eeg dataset, 10 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#