EEGdash›OpenNeuro›DS006890

Iss. 6890 · 2 subjects · 870 recordings · CC0

Dataset Brief · Longitudinal Multitask Wireless ECoG Data from Two Fully Impl…

DS006890: ieeg dataset, 2 subjects#

Longitudinal Multitask Wireless ECoG Data from Two Fully Implanted Macaca fuscata

Citation: Huixiang Yang, Ryohei Fukuma, Tomoyuki Namima, Kotaro Okuda, Asaya Nishi, Takamitsu Iwata, Abdi Reza, Kota S Sasaki, Taro Kaiju, Gurlal Gill, Haruhiko Kishima, Shinji Nishimoto, Takufumi Yanagisawa (—). Longitudinal Multitask Wireless ECoG Data from Two Fully Implanted Macaca fuscata. 10.18112/openneuro.ds006890.v1.0.0

2-participant iEEG dataset — Longitudinal Multitask Wireless ECoG Data from Two Fully Implanted Macaca fuscata.

Data & curation Huixiang Yang · Ryohei Fukuma · Tomoyuki Namima · Kotaro Okuda · Asaya Nishi · Takamitsu Iwata · …
Funding Grant JPMJER1801 from JST, JPMJMS2012 (TY) from Moonshot R&D, JPMJCR18A5 (TY) from CREST, JPMJCR24U2 (TY) from AIP

iEEG · 50 (471), 66 (399) ch1000 HzBIDS 1.9.05 tasks251 sessionsHealthyMultisensoryMotor

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 DS006890

dataset = DS006890(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 = DS006890(cache_dir="./data", subject="01")

Advanced query

dataset = DS006890(
    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{ds006890,
  title = {Longitudinal Multitask Wireless ECoG Data from Two Fully Implanted Macaca fuscata},
  author = {Huixiang Yang and Ryohei Fukuma and Tomoyuki Namima and Kotaro Okuda and Asaya Nishi and Takamitsu Iwata and Abdi Reza and Kota S Sasaki and Taro Kaiju and Gurlal Gill and Haruhiko Kishima and Shinji Nishimoto and Takufumi Yanagisawa},
  doi = {10.18112/openneuro.ds006890.v1.0.0},
  url = {https://doi.org/10.18112/openneuro.ds006890.v1.0.0},
}

§ 02Study · The README

About This Dataset#

Longitudinal Multitask Wireless ECoG Data from Two Fully Implanted Macaca fuscata — README

This repository contains a wireless subdural ECoG (iEEG) dataset from Macaca fuscata monkeys,

organized in compliance with the iEEG-BIDS specification.

Recordings were acquired several times each week using a wireless, inductively powered implant. The data were curated and organized in BIDS format to facilitate reproducible research in neuroscience. Keywords: wireless subdural ECoG, iEEG, Macaca fuscata, BIDS-compliant dataset, longitudinal recordings, task-based neurophysiology

BIDS Organization

dataset_description.json

participants.tsv, participants.json

README.md, CHANGES.md

sub-<id>/ses-<index>/ieeg/ (with *_ieeg.edf, *_ieeg.json, *_channels.tsv, *_events.tsv, *_scans.tsv, *_electrodes.tsv, *_electrodes.json, *_coordsystem.json)

View full README

Recordings were acquired several times each week using a wireless, inductively powered implant. The data were curated and organized in BIDS format to facilitate reproducible research in neuroscience. Keywords: wireless subdural ECoG, iEEG, Macaca fuscata, BIDS-compliant dataset, longitudinal recordings, task-based neurophysiology

BIDS Organization

dataset_description.json

participants.tsv, participants.json

README.md, CHANGES.md

sub-<id>/ses-<index>/ieeg/ (with *_ieeg.edf, *_ieeg.json, *_channels.tsv, *_events.tsv, *_scans.tsv, *_electrodes.tsv, *_electrodes.json, *_coordsystem.json)

Tasks

Tasks include rest, pressing, reaching, listening, sep.

Only curated and validated tasks are exported.

Signals and Channels

Uniform sampling rate per file.

channels.tsv lists physiological (ECoG), trigger (TRIGGER) and auxiliary channels (MISC).

Usage

This dataset can be loaded with BIDS-compatible toolboxes such as MNE-Python, FieldTrip, or EEGLAB.

Inspect *_events.tsv for task timing and *_channels.tsv for channel information.

Participants

Each subject corresponds to an individual monkey (e.g., sub-monkeyb, sub-monkeyc).

Ethics

All animal procedures complied with Japanese laws and institutional regulations, including the Science Council of Japan Guidelines for Proper Conduct of Animal Experiments and national standards on pain relief and euthanasia, and were approved by the Animal Experiment Committee — The University of Osaka (approval FBS-25-002).

License and Citation

License: CC BY 4.0 Citation: [Authors], “[Dataset Title],” [Repository/DOI], [Year].

Contact

Maintainer: Huixiang Yang, The University of Osaka, yanghuixiang@bci.med.osaka-u.ac.jp For issues, please use the repository issue tracker.

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Age distribution by gender (n=2, range 8–9 yr, mean 8.5 yr)

5

Female · 2

Sex composition

Female
2

Channel counts (ch)

5066

Sampling frequencies: 1000.0 Hz (n=870 recordings)

Total recording duration: 105 h

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 50 (471), 66 (399) ch · iEEG · 1000 Hz · 2 subjects, 870 recordings

Live trace viewer — sub-monkeyb · ses-day310 · task-rest · run-01

Showing one representative recording out of 2 subjects and 870 recordings in this dataset. Browse the full set on OpenNeuro; drop any other _ieeg.{set,edf,bdf,vhdr} file onto the viewer (or pass ?ieeg=<url>) to inspect it.

Electrode layout — iEEG · 32 sensors — 32 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.DS006890(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)Yang2025_Longitudinal

Canonical—

Importable asDS006890 · Yang2025_Longitudinal

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

class eegdash.dataset.DS006890(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Longitudinal Multitask Wireless ECoG Data from Two Fully Implanted Macaca fuscata

Study:: ds006890 (OpenNeuro)
Author (year):: Yang2025_Longitudinal
Canonical:: —

Also importable as: DS006890, Yang2025_Longitudinal.

Modality: ieeg; Experiment type: Motor; Subject type: Healthy. Subjects: 2; recordings: 870; tasks: 5.

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/ds006890 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006890 DOI: https://doi.org/10.18112/openneuro.ds006890.v1.0.0

Examples

>>> from eegdash.dataset import DS006890
>>> dataset = DS006890(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 FacePre-bundled mirror at EEGDash/ds006890 · pull with datasets.load_dataset("EEGDash/ds006890").huggingface

Croissant 1.0Machine-readable JSON-LD descriptor — DS006890.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 ds006890 to reproduce the tutorial on this dataset.

Citation

Huixiang Yang, Ryohei Fukuma, Tomoyuki Namima, Kotaro Okuda, Asaya Nishi, … (n.d.). Longitudinal Multitask Wireless ECoG Data from Two Fully Implanted Macaca fuscata. 10.18112/openneuro.ds006890.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.ds006890.v1.0.0.

Related & sibling datasets

DS004624IEEG · 3 subj DS005415IEEG · 13 subj NM000139EEG · 9 subj DS003688IEEG · 51 subj DS005691IEEG · 8 subj

+ 1 more — see See Also below →

BIDS

BIDS 1.9.0

Sidecars

events · events.json · channels · electrodes · coordsystem

Provenance

CC0 · 10.18112/openneuro.ds006890.v1.0.0

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HuggingFace

Dataset ID	`DS006890`
Title	Longitudinal Multitask Wireless ECoG Data from Two Fully Implanted Macaca fuscata
Author (year)	`Yang2025_Longitudinal`
Canonical	—
Importable as	`DS006890`, `Yang2025_Longitudinal`
Year	—
Authors	Huixiang Yang, Ryohei Fukuma, Tomoyuki Namima, Kotaro Okuda, Asaya Nishi, Takamitsu Iwata, Abdi Reza, Kota S Sasaki, Taro Kaiju, Gurlal Gill, Haruhiko Kishima, Shinji Nishimoto, Takufumi Yanagisawa
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds006890.v1.0.0
Source links	OpenNeuro \| NeMAR \| Source URL

DS006890: ieeg dataset, 2 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#