DS007630: eeg dataset, 3 subjects#

EEG-Speech Brain Decoding Dataset

Access recordings and metadata through EEGDash.

Citation: Motoshige Sato, Ilya Horiguchi, Masakazu Inoue, Kenichi Tomeoka, Eri Hatakeyama, Yuya Kita, Atsushi Yamamoto, Ippei Fujisawa, Shuntaro Sasai (2026). EEG-Speech Brain Decoding Dataset. 10.18112/openneuro.ds007630.v1.0.0

Modality: eeg Subjects: 3 Recordings: 1974 License: CC0 Source: openneuro

Metadata: Complete (100%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import DS007630

dataset = DS007630(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 = DS007630(cache_dir="./data", subject="01")

Advanced query

dataset = DS007630(
    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{ds007630,
  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.ds007630.v1.0.0},
  url = {https://doi.org/10.18112/openneuro.ds007630.v1.0.0},
}

About This Dataset#

EEG-Speech Brain Decoding Dataset

Overview

This dataset contains EEG recordings and audio data.

Sessions

View full README

EEG-Speech Brain Decoding Dataset

Overview

This dataset contains EEG recordings and audio data.

Sessions

Sessions are labeled by recording date in YYYYMMDD format. - Example: ses-20240401 = recorded on April 1, 2024

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

File Format Notes

EEG Data

Raw EEG data is stored: - Path: sub-*/ses-*/eeg/*_eeg.edf - Note: EDF format is not officially part of BIDS-EEG specification - Files are excluded in .bidsignore but documented here for reference - Future releases may include EDF conversions for full BIDS compliance

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-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)

Dataset Information#

Dataset ID	`DS007630`
Title	EEG-Speech Brain Decoding Dataset
Author (year)	—
Canonical	—
Importable as	`DS007630`
Year	2026
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.ds007630.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: 3
Recordings: 1974
Tasks: 3

Channels & sampling rate

Channels: 134 (1496), 90 (172), 140 (168), 70 (138)
Sampling rate (Hz): 1200.0 (1802), 1024.0 (161), 2048.0 (11)
Duration (hours): Not calculated

Electrode Layout#

No scalp electrode layout is currently indexed for this dataset. Once the eegdash montage registry ingests it, the interactive viewer will appear here automatically.

Dataset Statistics#

Sex distribution

4

Male Total: 4

Channel counts (ch)

7090134140

Sampling frequencies (Hz)

102412002048

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

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.

Files: —

Size: —

Subjects: —

Click to load file structure…

API Reference#

Use the DS007630 class to access this dataset programmatically.

class eegdash.dataset.DS007630(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

EEG-Speech Brain Decoding Dataset

Study:: ds007630 (OpenNeuro)
Author (year):: nan
Canonical:: —

Also importable as: DS007630, 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/ds007630 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds007630 DOI: https://doi.org/10.18112/openneuro.ds007630.v1.0.0

Examples

>>> from eegdash.dataset import DS007630
>>> dataset = DS007630(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)