DS007591: eeg dataset, 3 subjects#

Delineating neural contributions to EEG-based speech decoding

Access recordings and metadata through EEGDash.

Citation: Motoshige Sato, Yasuo Kabe, Sensho Nobe, Akito Yoshida, Masakazu Inoue, Mayumi Shimizu, Kenichi Tomeoka, Shuntaro Sasai (2026). Delineating neural contributions to EEG-based speech decoding. 10.18112/openneuro.ds007591.v1.0.1

Modality: eeg Subjects: 3 Recordings: 21 License: CC0 Source: openneuro

Metadata: Complete (100%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import DS007591

dataset = DS007591(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 = DS007591(cache_dir="./data", subject="01")

Advanced query

dataset = DS007591(
    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{ds007591,
  title = {Delineating neural contributions to EEG-based speech decoding},
  author = {Motoshige Sato and Yasuo Kabe and Sensho Nobe and Akito Yoshida and Masakazu Inoue and Mayumi Shimizu and Kenichi Tomeoka and Shuntaro Sasai},
  doi = {10.18112/openneuro.ds007591.v1.0.1},
  url = {https://doi.org/10.18112/openneuro.ds007591.v1.0.1},
}

About This Dataset#

Delineating neural contributions to EEG-based speech decoding

Overview

128-channel EEG recordings during speech production tasks. Participants produced one of 5 color words (green, magenta, orange, violet, yellow) under three speech conditions: overt, minimally overt, and covert. Each trial consists of 5 repetitions of the same word (1.25 sec per repetition).

Dataset Information#

Dataset ID	`DS007591`
Title	Delineating neural contributions to EEG-based speech decoding
Author (year)	`Sato2026_Delineating`
Canonical	—
Importable as	`DS007591`, `Sato2026_Delineating`
Year	2026
Authors	Motoshige Sato, Yasuo Kabe, Sensho Nobe, Akito Yoshida, Masakazu Inoue, Mayumi Shimizu, Kenichi Tomeoka, Shuntaro Sasai
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds007591.v1.0.1
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: 21
Tasks: 3

Channels & sampling rate

Channels: 139
Sampling rate (Hz): 256.0
Duration (hours): Not calculated

Electrode Layout#

Electrode layout — EEG · 128 sensors — 128 channels

Dataset Statistics#

Channel counts: 139 ch (n=21 recordings)

Sampling frequencies: 256.0 Hz (n=21 recordings)

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 DS007591 class to access this dataset programmatically.

class eegdash.dataset.DS007591(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

Delineating neural contributions to EEG-based speech decoding

Study:: ds007591 (OpenNeuro)
Author (year):: Sato2026_Delineating
Canonical:: —

Also importable as: DS007591, Sato2026_Delineating.

Modality: eeg; Experiment type: Motor; Subject type: Healthy. Subjects: 3; recordings: 21; 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/ds007591 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds007591 DOI: https://doi.org/10.18112/openneuro.ds007591.v1.0.1

Examples

>>> from eegdash.dataset import DS007591
>>> dataset = DS007591(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)