EEGdash›OpenNeuro›DS006465

Iss. 6465 · 20 subjects · 80 recordings · CC0

Dataset Brief · 3M-CPSEED：An EEG-based Dataset for Chinese Pinyin Production…

DS006465: eeg dataset, 20 subjects#

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

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

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

Data & curation Xinyu Ma · Jiang Yi · Ning Jiang
Funding a 1.3.5 project for disciplines of excellence from West China Hospital (#ZYYC22001)

EEG · 32 (58), 126 (19), 33 (3) ch500 HzBIDS 1.7.0Task · imaginedspeech4 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#

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},
}

§ 02Study · The README

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).

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Age distribution by gender (n=20, range 19–30 yr, mean 24.6 yr)

15202530

Female · 11Male · 9

Sex composition

Female
11
Male
9
F : M ratio
1.22 : 1

55% female · n = 20 subjects with reported sex.

HandednessRight · 20

Channel counts (ch)

3233126

Sampling frequencies: 500.0 Hz (n=80 recordings)

Total recording duration: 29 h

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 32 (58), 126 (19), 33 (3) ch · EEG · 500 Hz · 20 subjects, 80 recordings

Live trace viewer — sub-13 · ses-4 · task-imaginedspeech

Showing one representative recording out of 20 subjects and 80 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.

Electrode layout — EEG · 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.DS006465(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)Ma2025

Canonical—

Importable asDS006465 · Ma2025

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

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

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

Study:: ds006465 (OpenNeuro)
Author (year):: Ma2025
Canonical:: —

Also importable as: DS006465, Ma2025.

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 DOI: https://doi.org/10.18112/openneuro.ds006465.v2.0.0

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: 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/ds006465 · pull with datasets.load_dataset("EEGDash/ds006465").huggingface

Croissant 1.0Machine-readable JSON-LD descriptor — DS006465.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 ds006465 to reproduce the tutorial on this dataset.

Citation

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

Provenance

¹Contributed to openneuro in BIDS format.

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

³Persistent identifier: 10.18112/openneuro.ds006465.v2.0.0.

Related & sibling datasets

DS005170EEG · 5 subj DS007602EEG · 3 subj DS007221EEG · 84 subj DS006945EEG · 5 subj NM000161EEG · 20 subj

+ 1 more — see See Also below →

BIDS

BIDS 1.7.0

Sidecars

events · events.json · channels · eeg.json

Provenance

CC0 · 10.18112/openneuro.ds006465.v2.0.0

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HuggingFace

Dataset ID	`DS006465`
Title	3M-CPSEED：An EEG-based Dataset for Chinese Pinyin Production in Overt, Silent-intended, and Imagined Speech
Author (year)	`Ma2025`
Canonical	—
Importable as	`DS006465`, `Ma2025`
Year	—
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

DS006465: eeg dataset, 20 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#