EEGdash›OpenNeuro›DS007808

Iss. 7808 · 3 subjects · 1974 recordings · CC0

Dataset Brief · EEG-Speech Brain Decoding Dataset

DS007808: eeg dataset, 3 subjects#

EEG-Speech Brain Decoding Dataset

Citation: Motoshige Sato, Ilya Horiguchi, Masakazu Inoue, Kenichi Tomeoka, Eri Hatakeyama, Yuya Kita, Atsushi Yamamoto, Ippei Fujisawa, Shuntaro Sasai (20). EEG-Speech Brain Decoding Dataset. 10.18112/openneuro.ds007808.v1.0.0

3-participant EEG dataset — EEG-Speech Brain Decoding Dataset.

Data & curation Motoshige Sato · Ilya Horiguchi · Masakazu Inoue · Kenichi Tomeoka · Eri Hatakeyama · Yuya Kita · …
Year 20 · Distributed via OpenNeuro
Funding JST, Moonshot R&D Grant Number JPMJMS2012

EEG · 140 (1512), 90 (172), 139 (152), 75 (138) ch1200 Hz · mixedBIDS 1.9.03 tasks312 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 DS007808

dataset = DS007808(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 = DS007808(cache_dir="./data", subject="01")

Advanced query

dataset = DS007808(
    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{ds007808,
  title = {EEG-Speech Brain Decoding Dataset},
  author = {Motoshige Sato and Ilya Horiguchi and Masakazu Inoue and Kenichi Tomeoka and Eri Hatakeyama and Yuya Kita and Atsushi Yamamoto and Ippei Fujisawa and Shuntaro Sasai},
  doi = {10.18112/openneuro.ds007808.v1.0.0},
  url = {https://doi.org/10.18112/openneuro.ds007808.v1.0.0},
}

§ 02Study · The README

About This Dataset#

This dataset contains EEG recordings and audio data.

Sessions are labeled by recording date in YYYYMMDD format.

Example: ses-20240401 = recorded on April 1, 2024
EEG-Speech Brain Decoding Dataset

Overview

Multiple recordings on the same day are distinguished by run numbers: - run-N: Nth recording of the day

Tasks
View full README
EEG-Speech Brain Decoding Dataset

Overview

Multiple recordings on the same day are distinguished by run numbers: - run-N: Nth recording of the day

Tasks

speechopen: Overt speech production task - Participants vocalize visually presented text

listening: Auditory listening task - Participants listen to prerecorded speech stimuli

listeningcovert: Auditory listening followed by covert speech imagery

EEG Acquisition Devices

Recordings are split by acquisition label: - acq-pangolin: g.tec g.Pangolin. Stored channels include 128 EEG channels plus audio monitor, EOG, EMG, trigger, and in 140-channel recordings one auxiliary mastoid/reference channel that is not used for analysis. - acq-scarabeo: g.tec g.SCARABEO. Stored channels include 64 EEG-related channels, with channels 62 and 63 corresponding to mastoid electrodes, plus audio monitor, EOG, EMG, and trigger channels. - acq-eego: ANT Neuro eego sports. Stored channels include EEG channels 0..30 and 32..63, channel 31 as miscellaneous, audio monitor, EOG, upper- and lower-lip EMG, miscellaneous auxiliary channels, and trigger.

Run-level *_channels.tsv files provide the authoritative channel typing for each recording.

For g.Pangolin sessions, *_electrodes.tsv and *_coordsystem.json files provide 3D coordinates for EEG001..EEG128 from g.tec electrode digitization (electrodes_uhd.xml). Electrode coordinate files are not provided for g.SCARABEO or eego sports sessions because verified channel-to-position coordinate mappings are not available in this release.

File Format Notes

EEG Data

Raw EEG data is stored: - Path: sub-*/ses-*/eeg/*_eeg.edf - Note: EDF files are included as the raw EEG recordings for this BIDS-EEG dataset.

Behavioral Data (Audio)

Task audio files are stored in beh/ directories: - Speech production: sub-*/ses-*/beh/*_recording-vocal_beh.wav - Listening: sub-*/ses-*/beh/*_recording-audio_beh.wav - Note: Not officially part of BIDS-EEG spec, but included for analysis convenience - Excluded in .bidsignore

Directory Structure

dataset_root/

├── README (this file) ├── CHANGES (version history) ├── dataset_description.json (dataset metadata) ├── participants.tsv (participant information) ├── participants.json (participant column descriptions) ├── task-speechopen_acq-pangolin_eeg.json (speech production EEG metadata) ├── task-speechopen_acq-scarabeo_eeg.json (speech production EEG metadata) ├── task-speechopen_acq-eego_eeg.json (speech production EEG metadata) ├── task-listening_acq-pangolin_eeg.json (listening EEG metadata) ├── task-speechopen_acq-pangolin_events.json (speech production events column descriptions) ├── task-speechopen_acq-pangolin_recording-vocal_beh.json ├── task-listening_acq-pangolin_recording-audio_beh.json ├── .bidsignore (files to ignore in validation) │ ├── code/ (analysis and preprocessing code) │ ├── preprocessing/ (EEG and audio preprocessing) │ ├── training/ (model training scripts) │ ├── evaluation/ (evaluation metrics) │ └── bids/ (BIDS conversion scripts) │ ├── sub-01/ (participant data) │ └── ses-YYYYMMDD/ (session by date) │ ├── eeg/ (EEG recordings) │ └── beh/ (behavioral/audio data) │ └── derivatives/ (processed data) └── pipeline-standard/ (standard preprocessing)

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Age distribution by gender (n=3, range 22–44 yr, mean 32.7 yr)

203040

Male · 3

Sex composition

Male
3

HandednessRight · 3

Channel counts (ch)

7590139140

Sampling frequencies (Hz)

102412002048

Total recording duration: 1020 h

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 140 (1512), 90 (172), 139 (152), 75 (138) ch · EEG · 1200 Hz · mixed · 3 subjects, 1974 recordings

Live trace viewer — sub-01 · ses-20231213 · task-speechopen · run-01

Showing one representative recording out of 3 subjects and 1974 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 · 128 sensors — 128 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.DS007808(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)—

Canonical—

Importable asDS007808

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

class eegdash.dataset.DS007808(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

EEG-Speech Brain Decoding Dataset

Study:: ds007808 (OpenNeuro)
Author (year):: nan
Canonical:: —

Also importable as: DS007808, nan.

Modality: eeg. Subjects: 3; recordings: 1974; tasks: 3.

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/ds007808 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds007808 DOI: https://doi.org/10.18112/openneuro.ds007808.v1.0.0

Examples

>>> from eegdash.dataset import DS007808
>>> dataset = DS007808(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 — DS007808.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 ds007808 to reproduce the tutorial on this dataset.

Citation

Motoshige Sato, Ilya Horiguchi, Masakazu Inoue, Kenichi Tomeoka, Eri Hatakeyama, … (20). EEG-Speech Brain Decoding Dataset. 10.18112/openneuro.ds007808.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.ds007808.v1.0.0.

Related & sibling datasets

DS007630EEG · 3 subj DS007541EEG · 32 subj DS003816EEG · 48 subj DS007788EEG · 7 subj DS004952EEG · 10 subj

+ 1 more — see See Also below →

BIDS

BIDS 1.9.0

Sidecars

events · channels · electrodes · coordsystem · eeg.json

Provenance

CC0 · 10.18112/openneuro.ds007808.v1.0.0

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HF org

Dataset ID	`DS007808`
Title	EEG-Speech Brain Decoding Dataset
Author (year)	—
Canonical	—
Importable as	`DS007808`
Year	20
Authors	Motoshige Sato, Ilya Horiguchi, Masakazu Inoue, Kenichi Tomeoka, Eri Hatakeyama, Yuya Kita, Atsushi Yamamoto, Ippei Fujisawa, Shuntaro Sasai
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds007808.v1.0.0
Source links	OpenNeuro \| NeMAR \| Source URL

DS007808: eeg dataset, 3 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#