EEGdash›NeMAR›NM000259

Iss. 259 · 10 subjects · 60 recordings · CC0

Dataset Brief · Speier et al. 2017 — A comparison of stimulus types in online…

NM000259: eeg dataset, 10 subjects#

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

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

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

Data & curation William Speier · Corey Arnold · Aniket Deshpande · Nader Pouratian
Year 2017 · Distributed via NeMAR

EEG · 32 ch256 HzBIDS 1.9.0Task · p3002 sessionsHealthyVisualAttention

Layer 01Study

What was asked

Hypothesis, independent & dependent variables, paradigm, cohort, and the editorial caveats around what the recordings can and cannot answer.

Layer 02Signal · BIDS

What was recorded

Sidecars, channels & electrodes, coordinate system, event semantics, and quality stats from the NEMAR pipeline when available.

Layer 03Training · ML

What you can train on

Recommended access modes — MNE Raw, braindecode windows, PyTorch DataLoader — plus the targets the metadata makes addressable.

§ 01Access · Get started

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},
}

§ 02Study · The README

About This Dataset#

P300 speller dataset from Speier et al 2017.

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

Speier2017

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
View full README
Speier2017

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

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Channel counts: 32 ch (n=60 recordings)

Sampling frequencies: 256.0 Hz (n=60 recordings)

Total recording duration: 3 h 22 min

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 32 ch · EEG · 256 Hz · 10 subjects, 60 recordings

Live trace viewer — sub-1 · ses-0 · task-p300 · run-0

Showing one representative recording out of 10 subjects and 60 recordings in this dataset. Browse the full set on OpenNeuro; drop any other _eeg.{set,edf,bdf,vhdr} file onto the viewer (or pass ?eeg=<url>) to inspect it.

Electrode layout — EEG · 32 sensors — 32 channels

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

§ 05Manifest · BIDS tree

Manifest#

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.

Recordings—

Files—

Subjects—

Modalities—

Click to load file structure…

§ 06API · Programmatic access

API Reference#

Signature

eegdash.dataset

class

eegdash.dataset.NM000259(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)Speier2017

Canonical—

Importable asNM000259 · Speier2017

Sourceeegdash/dataset/registry.py · [source ↗]

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

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)

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.

Access modesMNE → braindecode → PyTorch → ML

.rawMNE Raw object — standard tools (filter, epoch, ICA, plot_psd).mne

BaseConcatDatasetEach record is a lazy BaseDataset from braindecode — windowed via create_windows_from_events.braindecode

DataLoaderWraps the windowed dataset into a PyTorch DataLoader; supports parallel workers and on-the-fly augmentations.pytorch

Zarr cacheOptional braindecode Zarr mirror for fast resume; persisted to cache_dir.zarr

Hugging FaceNo per-dataset mirror published yet — browse the EEGDash org listing for sibling datasets. See the datasets loader API.huggingface

Croissant 1.0Machine-readable JSON-LD descriptor — NM000259.croissant.json (MLCommons schema, ingestible by PyTorch / TensorFlow / JAX).mlcommons

Examples using EEGDashcurated · start here

Find datasets with the EEGDash APIQuery the catalogue, filter by task or modality, list candidates.

Load one EEG recordingResolve a single record to an MNE Raw with channels and events.

EEG recording to PyTorch DataLoaderWrap braindecode windows in a DataLoader for model training.

Preprocess EEG and create windowsFilter, resample, epoch — and persist the windowed dataset.

Save and reload prepared dataCache a windowed dataset to disk and reattach it without recompute.

Download a dataset locallyPrefetch BIDS files to a local cache and validate the layout.

Swap any load_dataset(...) call for nm000259 to reproduce the tutorial on this dataset.

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

Provenance

¹Contributed to nemar in BIDS format.

²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.

³Persistent identifier: 10.1371/journal.pone.0175382.

Related & sibling datasets

NM000188EEG · 10 subj NM000247EEG · 10 subj NM000186EEG · 8 subj NM000198EEG · 13 subj NM000249EEG · 13 subj

+ 1 more — see See Also below →

BIDS

BIDS 1.9.0

Sidecars

events · events.json · channels · eeg.json

Provenance

CC0 · 10.1371/journal.pone.0175382

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HF org

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

NM000259: eeg dataset, 10 subjects#

Quickstart#

About This Dataset#

Cohort#

Dataset Statistics#

Signal · Electrodes & live trace#

NEMAR Processing Statistics#

Manifest#

File Explorer#

API Reference#

Citation

Provenance

Related & sibling datasets

See Also#