DS006890#

Longitudinal Multitask Wireless ECoG Data from Two Fully Implanted Macaca fuscata

Access recordings and metadata through EEGDash.

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 (2025). Longitudinal Multitask Wireless ECoG Data from Two Fully Implanted Macaca fuscata. 10.18112/openneuro.ds006890.v1.0.0

Modality: ieeg Subjects: 2 Recordings: 5474 License: CC0 Source: openneuro

Metadata: Complete (100%)

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},
}

About This Dataset#

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

Overview

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,

Dataset Information#

Dataset ID	`DS006890`
Title	Longitudinal Multitask Wireless ECoG Data from Two Fully Implanted Macaca fuscata
Year	2025
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

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: 2
Recordings: 5474
Tasks: 5

Channels & sampling rate

Channels: 50 (942), 66 (798)
Sampling rate (Hz): 1000.0
Duration (hours): 0.0

API Reference#

Use the DS006890 class to access this dataset programmatically.

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

Bases: EEGDashDataset

OpenNeuro dataset ds006890. 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

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, overwrite=False)[source]#

Save the dataset to disk.

Parameters:

path (str or Path) – Destination file path.
overwrite (bool, default False) – If True, overwrite existing file.

Return type:

None