DS007602: eeg dataset, 3 subjects#

EEG-Speech Brain Decoding Dataset

Access recordings and metadata through EEGDash.

Citation: Motoshige Sato, Masakazu Inoue, Kenichi Tomeoka, Ilya Horiguchi, Eri Hatakeyama, Yuya Kita, Atsushi Yamamoto, Ippei Fujisawa, Shuntaro Sasai (2026). EEG-Speech Brain Decoding Dataset. 10.18112/openneuro.ds007602.v1.0.1

Modality: eeg Subjects: 3 Recordings: 113 License: CC0 Source: openneuro

Metadata: Complete (100%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import DS007602

dataset = DS007602(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 = DS007602(cache_dir="./data", subject="01")

Advanced query

dataset = DS007602(
    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{ds007602,
  title = {EEG-Speech Brain Decoding Dataset},
  author = {Motoshige Sato and Masakazu Inoue and Kenichi Tomeoka and Ilya Horiguchi and Eri Hatakeyama and Yuya Kita and Atsushi Yamamoto and Ippei Fujisawa and Shuntaro Sasai},
  doi = {10.18112/openneuro.ds007602.v1.0.1},
  url = {https://doi.org/10.18112/openneuro.ds007602.v1.0.1},
}

About This Dataset#

EEG-Speech Brain Decoding Dataset

Overview

This dataset contains EEG recordings and audio data.

Sessions

Dataset Information#

Dataset ID	`DS007602`
Title	EEG-Speech Brain Decoding Dataset
Author (year)	`Sato2026_Speech`
Canonical	—
Importable as	`DS007602`, `Sato2026_Speech`
Year	2026
Authors	Motoshige Sato, Masakazu Inoue, Kenichi Tomeoka, Ilya Horiguchi, Eri Hatakeyama, Yuya Kita, Atsushi Yamamoto, Ippei Fujisawa, Shuntaro Sasai
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds007602.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: 113
Tasks: 1

Channels & sampling rate

Channels: 134
Sampling rate (Hz): 1200.0
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: 134 ch (n=113 recordings)

Sampling frequencies: 1200.0 Hz (n=113 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 DS007602 class to access this dataset programmatically.

class eegdash.dataset.DS007602(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

EEG-Speech Brain Decoding Dataset

Study:: ds007602 (OpenNeuro)
Author (year):: Sato2026_Speech
Canonical:: —

Also importable as: DS007602, Sato2026_Speech.

Modality: eeg; Experiment type: Motor; Subject type: Healthy. Subjects: 3; recordings: 113; 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/ds007602 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds007602 DOI: https://doi.org/10.18112/openneuro.ds007602.v1.0.1

Examples

>>> from eegdash.dataset import DS007602
>>> dataset = DS007602(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)