NM000204: eeg dataset, 14 subjects#

Name: Bluetooth speaker experiment (14 subjects, 6 classes, 31 EEG ch)
Published: 2019
License: CC-BY-4.0

Bluetooth speaker experiment (14 subjects, 6 classes, 31 EEG ch)

Access recordings and metadata through EEGDash.

Citation: Jongmin Lee, Minju Kim, Dojin Heo, Jongsu Kim, Min-Ki Kim, Taejun Lee, Jongwoo Park, HyunYoung Kim, Minho Hwang, Laehyun Kim, Sung-Phil Kim (2019). Bluetooth speaker experiment (14 subjects, 6 classes, 31 EEG ch).

Modality: eeg Subjects: 14 Recordings: 420 License: CC-BY-4.0 Source: nemar

Metadata: Complete (90%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import NM000204

dataset = NM000204(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 = NM000204(cache_dir="./data", subject="01")

Advanced query

dataset = NM000204(
    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{nm000204,
  title = {Bluetooth speaker experiment (14 subjects, 6 classes, 31 EEG ch)},
  author = {Jongmin Lee and Minju Kim and Dojin Heo and Jongsu Kim and Min-Ki Kim and Taejun Lee and Jongwoo Park and HyunYoung Kim and Minho Hwang and Laehyun Kim and Sung-Phil Kim},
}

About This Dataset#

Bluetooth speaker experiment (14 subjects, 6 classes, 31 EEG ch)

Bluetooth speaker experiment (14 subjects, 6 classes, 31 EEG ch).

Dataset Overview

Code: Lee2024-BS
Paradigm: p300
DOI: 10.3389/fnhum.2024.1320457

View full README

Bluetooth speaker experiment (14 subjects, 6 classes, 31 EEG ch)

Bluetooth speaker experiment (14 subjects, 6 classes, 31 EEG ch).

Dataset Overview

Code: Lee2024-BS
Paradigm: p300
DOI: 10.3389/fnhum.2024.1320457
Subjects: 14
Sessions per subject: 1
Events: Target=2, NonTarget=1
Trial interval: [0, 1] s
File format: MATLAB

Acquisition

Sampling rate: 500.0 Hz
Number of channels: 31
Channel types: eeg=31
Channel names: Fp1, Fpz, Fp2, F7, F3, Fz, F4, F8, FT9, FC5, FC1, FC2, FC6, FT10, T7, C3, Cz, C4, T8, CP5, CP1, CP2, CP6, P7, P3, Pz, P4, P8, O1, Oz, O2
Montage: standard_1020
Hardware: actiCHamp (Brain Products)
Reference: linked mastoids
Sensor type: active
Line frequency: 60.0 Hz

Participants

Number of subjects: 14
Health status: healthy
Age: mean=22.64, std=3.08
Gender distribution: male=9, female=5
Species: human

Experimental Protocol

Paradigm: p300
Number of classes: 2
Class labels: Target, NonTarget
Trial duration: 1.0 s
Study design: P300 BCI for BS home appliance control; 6-class oddball; LCD display
Feedback type: visual
Stimulus type: flash
Stimulus modalities: visual
Primary modality: visual
Mode: online

HED Event Annotations

Schema: HED 8.4.0 | Browse: https://www.hedtags.org/hed-schema-browser

Target

     ├─ Sensory-event
     ├─ Experimental-stimulus
     ├─ Visual-presentation
     └─ Target

NonTarget

├─ Sensory-event
├─ Experimental-stimulus
├─ Visual-presentation
└─ Non-target

Paradigm-Specific Parameters

Detected paradigm: p300
Stimulus onset asynchrony: 750.0 ms

Data Structure

Trials: 50 training + 30 testing blocks per subject
Trials context: per_subject

BCI Application

Applications: home_appliance_control
Environment: laboratory
Online feedback: True

Tags

Pathology: Healthy
Modality: ERP
Type: P300

Documentation

DOI: 10.3389/fnhum.2024.1320457
License: CC-BY-4.0
Investigators: Jongmin Lee, Minju Kim, Dojin Heo, Jongsu Kim, Min-Ki Kim, Taejun Lee, Jongwoo Park, HyunYoung Kim, Minho Hwang, Laehyun Kim, Sung-Phil Kim
Institution: Ulsan National Institute of Science and Technology
Country: KR
Data URL: https://github.com/jml226/Home-Appliance-Control-Dataset
Publication year: 2024

References

Appelhoff, S., Sanderson, M., Brooks, T., Vliet, M., Quentin, R., Holdgraf, C., Chaumon, M., Mikulan, E., Tavabi, K., Hochenberger, R., Welke, D., Brunner, C., Rockhill, A., Larson, E., Gramfort, A. and Jas, M. (2019). MNE-BIDS: Organizing electrophysiological data into the BIDS format and facilitating their analysis. Journal of Open Source Software 4: (1896). https://doi.org/10.21105/joss.01896 Pernet, C. R., Appelhoff, S., Gorgolewski, K. J., Flandin, G., Phillips, C., Delorme, A., Oostenveld, R. (2019). EEG-BIDS, an extension to the brain imaging data structure for electroencephalography. Scientific Data, 6, 103. https://doi.org/10.1038/s41597-019-0104-8 Generated by MOABB 1.5.0 (Mother of All BCI Benchmarks) https://github.com/NeuroTechX/moabb

Dataset Information#

Dataset ID	`NM000204`
Title	Bluetooth speaker experiment (14 subjects, 6 classes, 31 EEG ch)
Author (year)	`Lee2024_Bluetooth_speaker_14`
Canonical	—
Importable as	`NM000204`, `Lee2024_Bluetooth_speaker_14`
Year	2019
Authors	Jongmin Lee, Minju Kim, Dojin Heo, Jongsu Kim, Min-Ki Kim, Taejun Lee, Jongwoo Park, HyunYoung Kim, Minho Hwang, Laehyun Kim, Sung-Phil Kim
License	CC-BY-4.0
Citation / DOI	Unknown
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: 14
Recordings: 420
Tasks: 1

Channels & sampling rate

Channels: 31
Sampling rate (Hz): 500.0
Duration (hours): 1.95331

API Reference#

Use the NM000204 class to access this dataset programmatically.

class eegdash.dataset.NM000204(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

Bluetooth speaker experiment (14 subjects, 6 classes, 31 EEG ch)

Study:: nm000204 (NeMAR)
Author (year):: Lee2024_Bluetooth_speaker_14
Canonical:: —

Also importable as: NM000204, Lee2024_Bluetooth_speaker_14.

Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 14; recordings: 420; 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/nm000204 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000204

Examples

>>> from eegdash.dataset import NM000204
>>> dataset = NM000204(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