DS006465#

3M-CPSEED：An EEG-based Dataset for Chinese Pinyin Production in Overt, Silent-intended, and Imagined Speech

Access recordings and metadata through EEGDash.

Citation: Xinyu Ma, Jiang Yi, Ning Jiang (2025). 3M-CPSEED：An EEG-based Dataset for Chinese Pinyin Production in Overt, Silent-intended, and Imagined Speech. 10.18112/openneuro.ds006465.v2.0.0

Modality: eeg Subjects: 20 Recordings: 564 License: CC0 Source: openneuro

Metadata: Complete (100%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import DS006465

dataset = DS006465(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 = DS006465(cache_dir="./data", subject="01")

Advanced query

dataset = DS006465(
    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{ds006465,
  title = {3M-CPSEED：An EEG-based Dataset for Chinese Pinyin Production in Overt, Silent-intended, and Imagined Speech},
  author = {Xinyu Ma and Jiang Yi and Ning Jiang},
  doi = {10.18112/openneuro.ds006465.v2.0.0},
  url = {https://doi.org/10.18112/openneuro.ds006465.v2.0.0},
}

About This Dataset#

Overview

This dataset, named 3M-CPSEED, consists of electroencephalogram (EEG) recordings obtained from 20 participants engaged in imagined speech tasks. 3M-CPSEED holds significant implications for speech neurophysiology research, not only facilitating exploration of neural activity differences across pinyin articulations but also enabling robust transfer learning studies for other alphabetic languages.

Data Collection

Participants: 20 healthy, right-handed individuals (average age: 24.55 years, standard deviation: 2.58 years; 11 females, 9 males) who are native Chinese speakers.

Materials: To strike a balance between comprehensively capturing the articulatory features of the Chinese phonological system and maintaining a concise, controllable set of stimuli, we selected this set of Pinyin sounds: Finals: “a, i, u, ü”; Initials: “m, f, j, l, k, ch”.

Procedure: Participants read Pinyin displayed on a screen at ‘speak’, ‘Silently articulated’ and ‘imagined’ phase. Each participant completed 4 blocks of 1600 trials in total.

Data Structure

The dataset is organized according to the BIDS standard:

Main Folder: dataset_description.json: Description of the dataset. participants.tsv: Participant information. participants.json: Details of columns in participants.tsv. README: General information about the dataset. data_all.mat: Labeled EEG data of all subjects in MAT format. Derivative Data: preproc/: Preprocessed data, including subfolders for each subject (sub-01, etc.), with data in .mat formats .

Acknowledgments This work was supported by a 1.3.5 project for disciplines of excellence from West China Hospital (#ZYYC22001).

Dataset Information#

Dataset ID	`DS006465`
Title	3M-CPSEED：An EEG-based Dataset for Chinese Pinyin Production in Overt, Silent-intended, and Imagined Speech
Year	2025
Authors	Xinyu Ma, Jiang Yi, Ning Jiang
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds006465.v2.0.0
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: 20
Recordings: 564
Tasks: 1

Channels & sampling rate

Channels: 32 (139), 126 (19), 33 (2)
Sampling rate (Hz): 500.0
Duration (hours): 0.0

API Reference#

Use the DS006465 class to access this dataset programmatically.

class eegdash.dataset.DS006465(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds006465. Modality: eeg; Experiment type: Motor; Subject type: Healthy. Subjects: 20; recordings: 80; 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/ds006465 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006465

Examples

>>> from eegdash.dataset import DS006465
>>> dataset = DS006465(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