EEGdash›OpenNeuro›DS005383
Iss. 5383 · 30 subjects · 240 recordings · CC0
Dataset Brief · TMNRED, A Chinese Language EEG Dataset for Fuzzy Semantic Tar…

DS005383: eeg dataset, 30 subjects#

TMNRED, A Chinese Language EEG Dataset for Fuzzy Semantic Target Identification in Natural Reading Environments

Citation: Yanru Bai, Qi Tang, Ran Zhao, Hongxing Liu, Mingkun Guo, Shuming Zhang, Minghan Guo, Junjie Wang, Changjian Wang, Mu Xing, Guangjian Ni, Dong Ming (—). TMNRED, A Chinese Language EEG Dataset for Fuzzy Semantic Target Identification in Natural Reading Environments. 10.18112/openneuro.ds005383.v1.0.0

30-participant EEG dataset — TMNRED, A Chinese Language EEG Dataset for Fuzzy Semantic Target Identification in Natural Reading Environments.

Data & curation Yanru Bai · Qi Tang · Ran Zhao · Hongxing Liu · Mingkun Guo · Shuming Zhang · …
EEG · 31 ch200 HzBIDS 1.7.0Task · fuzzysemanticrecognition8 sessionsHealthyVisualPerception
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 DS005383

dataset = DS005383(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Filter by subject

dataset = DS005383(cache_dir="./data", subject="01")

Advanced query

dataset = DS005383(
    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{ds005383,
  title = {TMNRED, A Chinese Language EEG Dataset for Fuzzy Semantic Target Identification in Natural Reading Environments},
  author = {Yanru Bai and Qi Tang and Ran Zhao and Hongxing Liu and Mingkun Guo and Shuming Zhang and Minghan Guo and Junjie Wang and Changjian Wang and Mu Xing and Guangjian Ni and Dong Ming},
  doi = {10.18112/openneuro.ds005383.v1.0.0},
  url = {https://doi.org/10.18112/openneuro.ds005383.v1.0.0},
}
§ 02Study · The README

About This Dataset#

This dataset, named TMNRED, consists of electroencephalogram (EEG) recordings obtained from 30 participants engaged in natural reading tasks. The aim is to investigate the mechanisms of semantic processing in the Chinese language within a natural reading environment.

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.

    TMNRED Dataset - Chinese Natural Reading EEG for Fuzzy Semantic Target Identification

    Overview

    • Derivative Data: - final_bids/: EEG data stored in JSON, TSV, and EDF formats. - preproc/: Preprocessed data, including subfolders for each subject (sub-01, etc.), with data in various formats (BDF, SET, FDT, ERP, MAT).

    Technical Validation

    Sensor-level EEG analyses were performed, showing distinct responses to target and non-target words at different time points, with notable changes in potential distribution across the scalp.

    Distribution

    The raw and preprocessed EEG data are openly available online at tym5049/TMNRED_Dataset under the Creative Commons Attribution 4.0 International Public License (https://creativecommons.org/licenses/by/4.0/).

    Usage Notes

    • Researchers should cite the dataset appropriately when using it.

    • For any questions or issues, please refer to the README file or contact the corresponding authors: Yanru Bai (yr56 bai@tju.edu.cn), Guangjian Ni (niguangjian@tju.edu.cn).

    Acknowledgments

    This work was mainly supported by the National Key R&D Program of China (2023YFF1203503) and the National Natural Science Foundation of China (82202290). We also thank all research assistants who provided general support in participant recruiting and data collection.

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Age distribution by gender (n=30, range 1–30 yr, mean 15.5 yr)

051015202530
Other · 30

Sex composition

30
subjects
Other
30

Channel counts: 31 ch (n=240 recordings)

Sampling frequencies: 200.0 Hz (n=240 recordings)

Total recording duration: 8 h 19 min

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 31 ch · EEG · 200 Hz · 30 subjects, 240 recordings
Live trace viewer — sub-13 · ses-4 · task-fuzzysemanticrecognition

Showing one representative recording out of 30 subjects and 240 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 · 30 sensors — 30 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.

NEMAR pipeline statistics — DS005383
HED event descriptors word cloud HED event descriptors word cloud — DS005383
§ 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

DS005383

Title

TMNRED, A Chinese Language EEG Dataset for Fuzzy Semantic Target Identification in Natural Reading Environments

Author (year)

Bai2024

Canonical

—

Importable as

DS005383, Bai2024

Year

—

Authors

Yanru Bai, Qi Tang, Ran Zhao, Hongxing Liu, Mingkun Guo, Shuming Zhang, Minghan Guo, Junjie Wang, Changjian Wang, Mu Xing, Guangjian Ni, Dong Ming

License

CC0

Citation / DOI

doi:10.18112/openneuro.ds005383.v1.0.0

Source links

OpenNeuro | NeMAR | Source URL
Copy-paste BibTeX
@dataset{ds005383,
  title = {TMNRED, A Chinese Language EEG Dataset for Fuzzy Semantic Target Identification in Natural Reading Environments},
  author = {Yanru Bai and Qi Tang and Ran Zhao and Hongxing Liu and Mingkun Guo and Shuming Zhang and Minghan Guo and Junjie Wang and Changjian Wang and Mu Xing and Guangjian Ni and Dong Ming},
  doi = {10.18112/openneuro.ds005383.v1.0.0},
  url = {https://doi.org/10.18112/openneuro.ds005383.v1.0.0},
}
§ 06API · Programmatic access

API Reference#

Signature
eegdash.dataset
class
eegdash.dataset.DS005383(cache_dir, query=None, s3_bucket=None, **kwargs)
Bases: EEGDashDataset
Author (year)Bai2024
Canonical—
Importable asDS005383 · Bai2024
Sourceeegdash/dataset/registry.py · [source ↗]
class eegdash.dataset.DS005383(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

TMNRED, A Chinese Language EEG Dataset for Fuzzy Semantic Target Identification in Natural Reading Environments

Study:

ds005383 (OpenNeuro)

Author (year):

Bai2024

Canonical:

—

Also importable as: DS005383, Bai2024.

Modality: eeg; Experiment type: Perception; Subject type: Healthy. Subjects: 30; recordings: 240; 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/ds005383 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005383 DOI: https://doi.org/10.18112/openneuro.ds005383.v1.0.0 NEMAR citation count: 0

Examples

>>> from eegdash.dataset import DS005383
>>> dataset = DS005383(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/ds005383 · pull with datasets.load_dataset("EEGDash/ds005383").huggingface
Croissant 1.0Machine-readable JSON-LD descriptor — DS005383.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 ds005383 to reproduce the tutorial on this dataset.

Citation

Yanru Bai, Qi Tang, Ran Zhao, Hongxing Liu, Mingkun Guo, … (n.d.). TMNRED, A Chinese Language EEG Dataset for Fuzzy Semantic Target Identification in Natural Reading Environments. 10.18112/openneuro.ds005383.v1.0.0

Provenance

¹Contributed to openneuro in BIDS format.

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

³Persistent identifier: 10.18112/openneuro.ds005383.v1.0.0.

BIDS
BIDS 1.7.0
Sidecars
events · events.json · channels · eeg.json
Machine-readable
schema.org/Dataset · Croissant

See Also#