EEGdash›OpenNeuro›DS005170
Iss. 5170 · 5 subjects · 225 recordings · CC0
Dataset Brief · Chisco

DS005170: eeg dataset, 5 subjects#

Chisco

Citation: Zihan Zhang, Yi Zhao, Yu Bao, Xiao Ding (—). Chisco. 10.18112/openneuro.ds005170.v1.1.2

5-participant EEG dataset — Chisco.

Data & curation Zihan Zhang · Yi Zhao · Yu Bao · Xiao Ding
Funding National Natural Science Foundation of China under Grants 62176079
BIDS 1.6.0Task · imagine6 sessionsHealthyVisualMotor
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#

Install

pip install eegdash

Access the data

from eegdash.dataset import DS005170

dataset = DS005170(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Filter by subject

dataset = DS005170(cache_dir="./data", subject="01")

Advanced query

dataset = DS005170(
    cache_dir="./data",
    query={"subject": {"$in": ["01", "02"]}},
)

Iterate recordings

for rec in dataset:
    print(rec.subject, rec.raw.info['sfreq'])

If you use this dataset in your research, please cite the original authors.

BibTeX

@dataset{ds005170,
  title = {Chisco},
  author = {Zihan Zhang and Yi Zhao and Yu Bao and Xiao Ding},
  doi = {10.18112/openneuro.ds005170.v1.1.2},
  url = {https://doi.org/10.18112/openneuro.ds005170.v1.1.2},
}
§ 02Study · The README

About This Dataset#

This dataset is a Chinese imagined speech dataset with five participants, identified as sub-01 to sub-05. The dataset includes raw data and preprocessed data in both fif and pkl formats. Information also can be found in zhangzihan-is-good/Chisco

The initial dataset release encompassed data from three participants (sub-01 to sub-03) as detailed in related Chisco publications. Subsequently, data from two additional subjects (sub-04 and sub-05) were incorporated. During the interval between the original dataset release and the addition of the new data, the BIDS protocol underwent updates. To preserve the integrity of the data processing code presented in our publications, the supplementary data continue to adhere to the previous version of the BIDS protocol. Consequently, the BIDS validator on our website may report errors; however, these do not compromise the usability of the dataset.

Chisco Dataset

Future releases will include data from sub-06 and sub-07, who participated under a new experimental paradigm. These will be published as part of a new dataset, Chisco 2.0. We invite you to stay tuned for further updates.

Dataset Structure

Root Directory

  • dataset_description.json

View full README

Chisco Dataset

Future releases will include data from sub-06 and sub-07, who participated under a new experimental paradigm. These will be published as part of a new dataset, Chisco 2.0. We invite you to stay tuned for further updates.

Dataset Structure

Root Directory

  • dataset_description.json

  • participants.tsv

  • README

  • derivatives/

  • sub-01/ to sub-05/

  • textdataset/

  • json/

Raw Data

The root directory contains folders sub-01 to sub-05 with raw data. Each participant’s folder contains 5-6 session folders, corresponding to data collected over 5-6 days.

Preprocessed Data

Preprocessed data is stored in the derivatives folder in both fif and pkl formats.

Text Data

The textdataset folder and json folder contain text data used to stimulate the participants.

File Structure

/Chisco
    /sub-01
        /ses-01
            /eeg
                sub-01_ses-01_task-imagine_eeg.edf

        ...

    /sub-02
        ...

    /sub-03
        ...

    /derivatives
        /fif
            /sub-01
                ...

            /sub-02
                ...

            /sub-03
                ...

        /pkl
            /sub-01
                ...

            /sub-02
                ...

            /sub-03
                ...

    /textdataset
        ...

    /json
        ...

    dataset_description.json
    README
    participants.tsv

License

This dataset is licensed under the CC0 license. You are free to use the dataset for non-commercial purposes, but the original author needs to be properly indicated.

Citation

If you use this dataset in your research, please cite the following link: zhangzihan-is-good/Chisco

Contact Information

For any questions, please contact the dataset authors.

Thank you for using the Chisco!

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Age distribution by gender (n=5, range 22–30 yr, mean 25.6 yr)

202530
Female · 2Male · 3

Sex composition

5
subjects
Female
2
Male
3
F : M ratio
0.67 : 1
40% female · n = 5 subjects with reported sex.
§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage — ch · EEG · Varies · 5 subjects, 225 recordings
Live trace viewer — sub-01 · ses-02 · task-imagine · run-012

Showing one representative recording out of 5 subjects and 225 recordings in this dataset. Browse the full set on OpenNeuro; drop any other _eeg.{set,edf,bdf,vhdr} file onto the viewer (or pass ?eeg=<url>) to inspect it.

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

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.

NEMAR pipeline statistics — DS005170
HED event descriptors word cloud HED event descriptors word cloud — DS005170
§ 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…
Full dataset metadata table

Dataset ID

DS005170

Title

Chisco

Author (year)

Zhang2024_Chisco

Canonical

—

Importable as

DS005170, Zhang2024_Chisco

Year

—

Authors

Zihan Zhang, Yi Zhao, Yu Bao, Xiao Ding

License

CC0

Citation / DOI

doi:10.18112/openneuro.ds005170.v1.1.2

Source links

OpenNeuro | NeMAR | Source URL
Copy-paste BibTeX
@dataset{ds005170,
  title = {Chisco},
  author = {Zihan Zhang and Yi Zhao and Yu Bao and Xiao Ding},
  doi = {10.18112/openneuro.ds005170.v1.1.2},
  url = {https://doi.org/10.18112/openneuro.ds005170.v1.1.2},
}
§ 06API · Programmatic access

API Reference#

Signature
eegdash.dataset
class
eegdash.dataset.DS005170(cache_dir, query=None, s3_bucket=None, **kwargs)
Bases: EEGDashDataset
Author (year)Zhang2024_Chisco
Canonical—
Importable asDS005170 · Zhang2024_Chisco
Sourceeegdash/dataset/registry.py · [source ↗]
class eegdash.dataset.DS005170(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Chisco

Study:

ds005170 (OpenNeuro)

Author (year):

Zhang2024_Chisco

Canonical:

—

Also importable as: DS005170, Zhang2024_Chisco.

Modality: eeg; Experiment type: Motor; Subject type: Healthy. Subjects: 5; recordings: 225; 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/ds005170 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005170 DOI: https://doi.org/10.18112/openneuro.ds005170.v1.1.2 NEMAR citation count: 1

Examples

>>> from eegdash.dataset import DS005170
>>> dataset = DS005170(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/ds005170 · pull with datasets.load_dataset("EEGDash/ds005170").huggingface
Croissant 1.0Machine-readable JSON-LD descriptor — DS005170.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 ds005170 to reproduce the tutorial on this dataset.

Citation

Zihan Zhang, Yi Zhao, Yu Bao, Xiao Ding (n.d.). Chisco. 10.18112/openneuro.ds005170.v1.1.2

Provenance

¹Contributed to openneuro in BIDS format.

²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.

³Persistent identifier: 10.18112/openneuro.ds005170.v1.1.2.

BIDS
BIDS 1.6.0
Sidecars
not yet probed
Machine-readable
schema.org/Dataset · Croissant

See Also#