NM000222: eeg dataset, 10 subjects#

Air conditioner control experiment (10 subjects, 4 classes, 25 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). Air conditioner control experiment (10 subjects, 4 classes, 25 EEG ch).

Modality: eeg Subjects: 10 Recordings: 305 License: CC-BY-4.0 Source: nemar

Metadata: Complete (90%)

Quickstart#

Install

pip install eegdash

Access the data

from eegdash.dataset import NM000222

dataset = NM000222(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Filter by subject

dataset = NM000222(cache_dir="./data", subject="01")

Advanced query

dataset = NM000222(
    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{nm000222,
  title = {Air conditioner control experiment (10 subjects, 4 classes, 25 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#

Air conditioner control experiment (10 subjects, 4 classes, 25 EEG ch)

Air conditioner control experiment (10 subjects, 4 classes, 25 EEG ch).

Dataset Overview

  • Code: Lee2024-AC

  • Paradigm: p300

  • DOI: 10.3389/fnhum.2024.1320457

View full README

Air conditioner control experiment (10 subjects, 4 classes, 25 EEG ch)

Air conditioner control experiment (10 subjects, 4 classes, 25 EEG ch).

Dataset Overview

  • Code: Lee2024-AC

  • Paradigm: p300

  • DOI: 10.3389/fnhum.2024.1320457

  • Subjects: 10

  • 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: 25

  • Channel types: eeg=25

  • Channel names: Fp1, Fpz, Fp2, F7, F3, Fz, F4, F8, FC5, FC1, FC2, FC6, C3, Cz, C4, CP5, CP1, CP2, CP6, P3, Pz, P4, 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: 10

  • Health status: healthy

  • Age: mean=22.4, std=2.59

  • Gender distribution: male=6, female=4

  • Species: human

Experimental Protocol

  • Paradigm: p300

  • Number of classes: 2

  • Class labels: Target, NonTarget

  • Trial duration: 1.0 s

  • Study design: P300 BCI for AC home appliance control; 4-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: 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) NeuroTechX/moabb

Dataset Information#

Dataset ID

NM000222

Title

Air conditioner control experiment (10 subjects, 4 classes, 25 EEG ch)

Author (year)

Lee2024_Air_conditioner_control

Canonical

—

Importable as

NM000222, Lee2024_Air_conditioner_control

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: 10

  • Recordings: 305

  • Tasks: 1

Channels & sampling rate

  • Channels: 25

  • Sampling rate (Hz): 500.0

  • Duration (hours): 3.1864966666666668

Tags

  • Pathology: Healthy

  • Modality: Visual

  • Type: Attention

Files & format

  • Size on disk: 415.3 MB

  • File count: 305

  • Format: BIDS

License & citation

  • License: CC-BY-4.0

  • DOI: —

Provenance

Electrode Layout#

Electrode layout — EEG · 25 sensors — 25 channels

Dataset Statistics#

Age distribution (n=10, range 22–22 yr)

20

Channel counts: 25 ch (n=305 recordings)

Sampling frequencies: 500.0 Hz (n=305 recordings)

Total recording duration: 3 h 11 min

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 — NM000222
HED event descriptors word cloud HED event descriptors word cloud — NM000222

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.

Files: —
Size: —
Subjects: —
Click to load file structure…

API Reference#

Use the NM000222 class to access this dataset programmatically.

class eegdash.dataset.NM000222(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

Air conditioner control experiment (10 subjects, 4 classes, 25 EEG ch)

Study:

nm000222 (NeMAR)

Author (year):

Lee2024_Air_conditioner_control

Canonical:

—

Also importable as: NM000222, Lee2024_Air_conditioner_control.

Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 10; recordings: 305; 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/nm000222 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000222

Examples

>>> from eegdash.dataset import NM000222
>>> dataset = NM000222(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.

See Also#