DS005170#

Chisco

Access recordings and metadata through EEGDash.

Citation: Zihan Zhang, Yi Zhao, Yu Bao, Xiao Ding (2024). Chisco. 10.18112/openneuro.ds005170.v1.1.2

Modality: eeg Subjects: 5 Recordings: 229 License: CC0 Source: openneuro Citations: 1.0

Metadata: Good (80%)

Quickstart#

Get Started

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)

Query & Filter

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'])

Cite This Dataset

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},
}

About This Dataset#

Chisco 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

Supplementary Information

View full README

Chisco 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

Supplementary Information

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.

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!

Dataset Information#

Dataset ID	`DS005170`
Title	Chisco
Year	2024
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

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: 5
Recordings: 229
Tasks: —

Channels & sampling rate

Channels: Varies
Sampling rate (Hz): Varies
Duration (hours): 0.0

API Reference#

Use the DS005170 class to access this dataset programmatically.

class eegdash.dataset.DS005170(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds005170. Modality: eeg; Experiment type: other. 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

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, 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