DS005383#

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

Access recordings and metadata through EEGDash.

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 (2024). TMNRED, A Chinese Language EEG Dataset for Fuzzy Semantic Target Identification in Natural Reading Environments. 10.18112/openneuro.ds005383.v1.0.0

Modality: eeg Subjects: 30 Recordings: 1925 License: CC0 Source: openneuro Citations: 0.0

Metadata: Complete (100%)

Quickstart#

Get Started

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)

Query & Filter

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'])

Cite This Dataset

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},
}

About This Dataset#

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

Overview

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.

Data Collection

View full README

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

Overview

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.

Data Collection

Participants: 30 healthy, right-handed individuals (average age: 22.07 years, standard deviation: 2.7 years; 18 females, 12 males) who are native Chinese speakers.
Materials: Text ranging from 15 to 20 characters, presented as news headlines or short sentences. Materials include target semantic items and non-target semantic items.
Procedure: Participants read sentences displayed on a screen at their own pace. Each participant completed 8 blocks of 400 trials in total, with each trial lasting approximately 2.2 seconds, including a fixation cross and inter-stimulus intervals.

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: - 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.

Dataset Information#

Dataset ID	`DS005383`
Title	TMNRED, A Chinese Language EEG Dataset for Fuzzy Semantic Target Identification in Natural Reading Environments
Year	2024
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

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: 30
Recordings: 1925
Tasks: 1

Channels & sampling rate

Channels: 30 (240), 31 (240)
Sampling rate (Hz): 200.0
Duration (hours): 0.0

API Reference#

Use the DS005383 class to access this dataset programmatically.

class eegdash.dataset.DS005383(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds005383. 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

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, 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