NM000323: eeg dataset, 54 subjects#

Lee2019-ERP

Access recordings and metadata through EEGDash.

Citation: Min-Ho Lee, O-Yeon Kwon, Yong-Jeong Kim, Hong-Kyung Kim, Young-Eun Lee, John Williamson, Siamac Fazli, Seong-Whan Lee (2019). Lee2019-ERP. 10.1093/gigascience/giz002

Modality: eeg Subjects: 54 Recordings: 216 License: GPL-3.0 Source: nemar

Metadata: Complete (100%)

Quickstart#

Install

pip install eegdash

Access the data

from eegdash.dataset import NM000323

dataset = NM000323(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Filter by subject

dataset = NM000323(cache_dir="./data", subject="01")

Advanced query

dataset = NM000323(
    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{nm000323,
  title = {Lee2019-ERP},
  author = {Min-Ho Lee and O-Yeon Kwon and Yong-Jeong Kim and Hong-Kyung Kim and Young-Eun Lee and John Williamson and Siamac Fazli and Seong-Whan Lee},
  doi = {10.1093/gigascience/giz002},
  url = {https://doi.org/10.1093/gigascience/giz002},
}

About This Dataset#

Lee2019-ERP

BMI/OpenBMI dataset for P300.

Dataset Overview

Code: Lee2019-ERP Paradigm: p300 DOI: 10.5524/100542

View full README

Lee2019-ERP

BMI/OpenBMI dataset for P300.

Dataset Overview

Code: Lee2019-ERP Paradigm: p300 DOI: 10.5524/100542 Subjects: 54 Sessions per subject: 2 Events: Target=1, NonTarget=2 Trial interval: [0.0, 1.0] s Runs per session: 2 File format: MAT

Acquisition

Sampling rate: 1000.0 Hz Number of channels: 62 Channel types: eeg=62, emg=4 Channel names: AF3, AF4, AF7, AF8, C1, C2, C3, C4, C5, C6, CP1, CP2, CP3, CP4, CP5, CP6, CPz, Cz, EMG1, EMG2, EMG3, EMG4, F10, F3, F4, F7, F8, F9, FC1, FC2, FC3, FC4, FC5, FC6, FT10, FT9, FTT10h, FTT9h, Fp1, Fp2, Fz, O1, O2, Oz, P1, P2, P3, P4, P7, P8, PO10, PO3, PO4, PO9, POz, Pz, T7, T8, TP10, TP7, TP8, TP9, TPP10h, TPP8h, TPP9h, TTP7h Montage: standard_1005 Hardware: BrainAmp Software: OpenBMI Reference: nasion Ground: AFz Sensor type: Ag/AgCl Line frequency: 60.0 Hz Impedance threshold: 10 kOhm Cap manufacturer: Brain Products Auxiliary channels: EMG (4 ch)

Participants

Number of subjects: 54 Health status: healthy Age: mean=29.5, min=24, max=35 Gender distribution: female=25, male=29 Handedness: right BCI experience: mixed Species: human

Experimental Protocol

Paradigm: p300 Task type: copy_spelling Number of classes: 2 Class labels: Target, NonTarget Study design: 36-symbol ERP row-column speller with random-set presentation and face stimuli, offline training and online test phases Feedback type: visual Stimulus type: rc_speller Stimulus modalities: visual Primary modality: visual Mode: offline Training/test split: True Instructions: Subjects were asked to copy-spell given sentences by gazing at target characters on screen. In training: ‘NEURAL NETWORKS AND DEEP LEARNING’ (33 characters), in test: ‘PATTERN RECOGNITION MACHINE LEARNING’ (36 characters). Participants counted number of times each target character flashed.

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 Number of targets: 36 Number of repetitions: 5 Inter-stimulus interval: 135.0 ms Stimulus onset asynchrony: 215.0 ms

Data Structure

Trials: {‘training’: 1980, ‘test’: 2160} Trials context: Training: copy-spell ‘NEURAL NETWORKS AND DEEP LEARNING’ (33 characters). Test: copy-spell ‘PATTERN RECOGNITION MACHINE LEARNING’ (36 characters). Each character received 5 sequences of 12 flashes (60 flashes total).

Preprocessing

Data state: raw Preprocessing applied: False

Signal Processing

Classifiers: LDA Feature extraction: Mean Amplitudes

Cross-Validation

Method: training-test split Evaluation type: within_session, cross_session

Performance (Original Study)

Accuracy: 96.7% Accuracy Std: 0.05 Illiteracy Rate: 11.1

BCI Application

Applications: speller, communication Online feedback: True

Tags

Pathology: Healthy Modality: Visual Type: Perception

Documentation

Description: EEG dataset and OpenBMI toolbox for three BCI paradigms: an investigation into BCI illiteracy DOI: 10.1093/gigascience/giz002 License: GPL-3.0 Investigators: Min-Ho Lee, O-Yeon Kwon, Yong-Jeong Kim, Hong-Kyung Kim, Young-Eun Lee, John Williamson, Siamac Fazli, Seong-Whan Lee Senior author: Seong-Whan Lee Contact: sw.lee@korea.ac.kr; Tel: +82-2-3290-3197; Fax: +82-2-3290-3583 Institution: Korea University Department: Department of Brain and Cognitive Engineering Address: 145 Anam-ro, Seongbuk-gu, Seoul, 02841, Korea Country: KR Repository: GigaDB Publication year: 2019 Keywords: EEG datasets, brain-computer interface, event-related potential, steady-state visually evoked potential, motor-imagery, OpenBMI toolbox, BCI illiteracy

References

Lee, M. H., Kwon, O. Y., Kim, Y. J., Kim, H. K., Lee, Y. E., Williamson, J., … Lee, S. W. (2019). EEG dataset and OpenBMI toolbox for three BCI paradigms: An investigation into BCI illiteracy. GigaScience, 8(5), 1–16. https://doi.org/10.1093/gigascience/giz002 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

NM000323

Title

Lee2019-ERP

Author (year)

Lee2019_ERP

Canonical

OpenBMI_ERP, OpenBMI_P300

Importable as

NM000323, Lee2019_ERP, OpenBMI_ERP, OpenBMI_P300

Year

2019

Authors

Min-Ho Lee, O-Yeon Kwon, Yong-Jeong Kim, Hong-Kyung Kim, Young-Eun Lee, John Williamson, Siamac Fazli, Seong-Whan Lee

License

GPL-3.0

Citation / DOI

doi:10.1093/gigascience/giz002

Source links

OpenNeuro | NeMAR | Source URL
Copy-paste BibTeX
@dataset{nm000323,
  title = {Lee2019-ERP},
  author = {Min-Ho Lee and O-Yeon Kwon and Yong-Jeong Kim and Hong-Kyung Kim and Young-Eun Lee and John Williamson and Siamac Fazli and Seong-Whan Lee},
  doi = {10.1093/gigascience/giz002},
  url = {https://doi.org/10.1093/gigascience/giz002},
}

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: 54

  • Recordings: 216

  • Tasks: 1

Channels & sampling rate

  • Channels: 66

  • Sampling rate (Hz): 1000.0

  • Duration (hours): 58.12466222222223

Tags

  • Pathology: Healthy

  • Modality: Visual

  • Type: Attention

Files & format

  • Size on disk: 38.6 GB

  • File count: 216

  • Format: BIDS

License & citation

  • License: GPL-3.0

  • DOI: doi:10.1093/gigascience/giz002

Provenance

API Reference#

Use the NM000323 class to access this dataset programmatically.

class eegdash.dataset.NM000323(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

Lee2019-ERP

Study:

nm000323 (NeMAR)

Author (year):

Lee2019_ERP

Canonical:

OpenBMI_ERP, OpenBMI_P300

Also importable as: NM000323, Lee2019_ERP, OpenBMI_ERP, OpenBMI_P300.

Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 54; recordings: 216; 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/nm000323 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000323 DOI: https://doi.org/10.1093/gigascience/giz002

Examples

>>> from eegdash.dataset import NM000323
>>> dataset = NM000323(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

See Also#