NM000259: eeg dataset, 10 subjects#

Speier et al. 2017 — A comparison of stimulus types in online classification of the P300 speller using language models

Access recordings and metadata through EEGDash.

Citation: William Speier, Corey Arnold, Aniket Deshpande, Nader Pouratian (2017). Speier et al. 2017 — A comparison of stimulus types in online classification of the P300 speller using language models. 10.1371/journal.pone.0175382

Modality: eeg Subjects: 10 Recordings: 60 License: CC0 Source: nemar

Metadata: Complete (100%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import NM000259

dataset = NM000259(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 = NM000259(cache_dir="./data", subject="01")

Advanced query

dataset = NM000259(
    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{nm000259,
  title = {Speier et al. 2017 — A comparison of stimulus types in online classification of the P300 speller using language models},
  author = {William Speier and Corey Arnold and Aniket Deshpande and Nader Pouratian},
  doi = {10.1371/journal.pone.0175382},
  url = {https://doi.org/10.1371/journal.pone.0175382},
}

About This Dataset#

Speier2017

P300 speller dataset from Speier et al 2017.

Dataset Overview

Code: Speier2017 Paradigm: p300 DOI: 10.1371/journal.pone.0175382

View full README

Speier2017

P300 speller dataset from Speier et al 2017.

Dataset Overview

Code: Speier2017 Paradigm: p300 DOI: 10.1371/journal.pone.0175382 Subjects: 10 Sessions per subject: 2 Events: Target=2, NonTarget=1 Trial interval: [0, 0.8] s Runs per session: 3 File format: BCI2000

Acquisition

Sampling rate: 256.0 Hz Number of channels: 32 Channel types: eeg=32 Channel names: Fz, FC1, FCz, FC2, FC4, FC6, C4, C6, CP4, CP6, FC3, FC5, C3, C5, CP3, CP5, CP1, P1, Cz, CPz, Pz, POz, CP2, P2, PO7, PO3, O1, Oz, Iz, O2, PO4, PO8 Montage: standard_1005 Hardware: g.tec amplifier Reference: left ear Ground: AFz Line frequency: 60.0 Hz

Participants

Number of subjects: 10 Health status: healthy Age: min=20, max=35 Species: human

Experimental Protocol

Paradigm: p300 Number of classes: 2 Class labels: Target, NonTarget Trial duration: 1.0 s Study design: P300 row-column speller; 2 stimulus conditions (Famous Faces, Inverting); 6x6 character matrix Feedback type: visual Stimulus type: flash / famous face overlay 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 Inter-stimulus interval: 25.0 ms Stimulus onset asynchrony: 125.0 ms

Data Structure

Trials: ~1200 flashes per training run (10 chars x 10 seq x 12) Trials context: per_run

Tags

Pathology: Healthy Modality: ERP Type: P300

Documentation

DOI: 10.1371/journal.pone.0175382 License: CC0 Investigators: William Speier, Corey Arnold, Aniket Deshpande, Nader Pouratian Institution: University of California, Los Angeles Country: US Data URL: https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/PHHHB6 Publication year: 2017

References

Speier, W., Deshpande, A., & Pouratian, N. (2017). A comparison of stimulus types in online classification of the P300 speller using language models. PLoS ONE, 12(4), e0175382. https://doi.org/10.1371/journal.pone.0175382 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	`NM000259`
Title	Speier et al. 2017 — A comparison of stimulus types in online classification of the P300 speller using language models
Author (year)	`Speier2017`
Canonical	—
Importable as	`NM000259`, `Speier2017`
Year	2017
Authors	William Speier, Corey Arnold, Aniket Deshpande, Nader Pouratian
License	CC0
Citation / DOI	doi:10.1371/journal.pone.0175382
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: 60
Tasks: 1

Channels & sampling rate

Channels: 32
Sampling rate (Hz): 256.0
Duration (hours): 3.3766015625

Electrode Layout#

Electrode layout — EEG · 32 sensors — 32 channels

Dataset Statistics#

Channel counts: 32 ch (n=60 recordings)

Sampling frequencies: 256.0 Hz (n=60 recordings)

Total recording duration: 3 h 22 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.

HED event descriptors word cloud

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 NM000259 class to access this dataset programmatically.

class eegdash.dataset.NM000259(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

Speier et al. 2017 — A comparison of stimulus types in online classification of the P300 speller using language models

Study:: nm000259 (NeMAR)
Author (year):: Speier2017
Canonical:: —

Also importable as: NM000259, Speier2017.

Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 10; recordings: 60; 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/nm000259 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000259 DOI: https://doi.org/10.1371/journal.pone.0175382

Examples

>>> from eegdash.dataset import NM000259
>>> dataset = NM000259(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)