EEGdash›NeMAR›NM000231

Iss. 231 · 8 subjects · 192 recordings · See source

Dataset Brief · P300 dataset from Hoffmann et al 2008

NM000231: eeg dataset, 8 subjects#

P300 dataset from Hoffmann et al 2008

Citation: Ulrich Hoffmann, Jean-Marc Vesin, Touradj Ebrahimi, Karin Diserens (2019). P300 dataset from Hoffmann et al 2008.

8-participant EEG dataset — P300 dataset from Hoffmann et al 2008.

Data & curation Ulrich Hoffmann · Jean-Marc Vesin · Touradj Ebrahimi · Karin Diserens
Year 2019 · Distributed via NeMAR
Funding Swiss National Science Foundation Grant No. 200020-112313

EEG · 32 ch2048 HzBIDS 1.9.0Task · p3004 sessionsOtherVisualAttention

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 NM000231

dataset = NM000231(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 = NM000231(cache_dir="./data", subject="01")

Advanced query

dataset = NM000231(
    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{nm000231,
  title = {P300 dataset from Hoffmann et al 2008},
  author = {Ulrich Hoffmann and Jean-Marc Vesin and Touradj Ebrahimi and Karin Diserens},
}

§ 02Study · The README

About This Dataset#

P300 dataset from Hoffmann et al 2008.

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

P300 dataset from Hoffmann et al 2008

Target

├─ Sensory-event
├─ Experimental-stimulus

View full README

P300 dataset from Hoffmann et al 2008

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: 6
Inter-stimulus interval: 400.0 ms
Stimulus onset asynchrony: 400.0 ms

Data Structure

Trials: {‘target’: 135, ‘non-target’: 675}
Trials per class: target=135, non-target=675
Trials context: per_session

Preprocessing

Data state: raw
Preprocessing applied: False

Signal Processing

Classifiers: BLDA, FLDA
Feature extraction: temporal samples from selected electrodes
Frequency bands: analyzed=[1.0, 12.0] Hz

Cross-Validation

Method: leave-one-session-out
Folds: 4
Evaluation type: session-based

Performance (Original Study)

Accuracy: 100.0%
Itr: 28.8 bits/min
Max Bitrate Disabled Avg: 19.0
Max Bitrate Able Bodied Avg: 38.6
Max Bitrate Overall Avg: 28.8

BCI Application

Applications: environment_control
Environment: laboratory
Online feedback: False

Tags

Pathology: Healthy, Cerebral palsy, Multiple sclerosis, Amyotrophic lateral sclerosis, Traumatic brain injury, Post-anoxic encephalopathy
Modality: Visual
Type: Research

Documentation

DOI: 10.1016/j.jneumeth.2007.03.005
License: Unknown
Investigators: Ulrich Hoffmann, Jean-Marc Vesin, Touradj Ebrahimi, Karin Diserens
Senior author: Karin Diserens
Contact: ulrich.hoffmann@epfl.ch
Institution: Ecole Polytechnique Fédérale de Lausanne
Department: Signal Processing Institute
Address: Signal Processing Institute, CH-1015 Lausanne, Switzerland
Country: CH
Repository: http://bci.epfl.ch/p300
Publication year: 2008
Funding: Swiss National Science Foundation Grant No. 200020-112313
Keywords: Brain–computer interface, P300, Disabled subjects, Fisher’s linear discriminant analysis, Bayesian linear discriminant analysis

References

Hoffmann, U., Vesin, J-M., Ebrahimi, T., Diserens, K., 2008. An efficient P300-based brain-computer interfacefor disabled subjects. Journal of Neuroscience Methods . https://doi.org/10.1016/j.jneumeth.2007.03.005 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#

Age distribution by gender (n=8, range 30–56 yr, mean 39.4 yr)

3035455055

Other · 8

Channel counts: 32 ch (n=192 recordings)

Sampling frequencies: 2048.0 Hz (n=192 recordings)

Total recording duration: 2 h 56 min

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 32 ch · EEG · 2048 Hz · 8 subjects, 192 recordings

Live trace viewer — sub-2 · ses-1 · task-p300 · run-1

Showing one representative recording out of 8 subjects and 192 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.NM000231(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)Hoffmann2008

Canonical—

Importable asNM000231 · Hoffmann2008

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

class eegdash.dataset.NM000231(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

P300 dataset from Hoffmann et al 2008

Study:: nm000231 (NeMAR)
Author (year):: Hoffmann2008
Canonical:: —

Also importable as: NM000231, Hoffmann2008.

Modality: eeg; Experiment type: Attention; Subject type: Other. Subjects: 8; recordings: 192; 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/nm000231 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000231

Examples

>>> from eegdash.dataset import NM000231
>>> dataset = NM000231(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 — NM000231.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 nm000231 to reproduce the tutorial on this dataset.

Citation

Ulrich Hoffmann, Jean-Marc Vesin, Touradj Ebrahimi, Karin Diserens (2019). P300 dataset from Hoffmann et al 2008.

Provenance

¹Contributed to nemar in BIDS format.

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

Related & sibling datasets

NM000187EEG · 8 subj NM000191EEG · 10 subj NM000351EEG · 19 subj NM000248EEG · 11 subj NM000169EEG · 8 subj

+ 1 more — see See Also below →

BIDS

BIDS 1.9.0

Sidecars

events · events.json · channels · eeg.json

Provenance

See source · DOI not on file

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HF org

Dataset ID	`NM000231`
Title	P300 dataset from Hoffmann et al 2008
Author (year)	`Hoffmann2008`
Canonical	—
Importable as	`NM000231`, `Hoffmann2008`
Year	2019
Authors	Ulrich Hoffmann, Jean-Marc Vesin, Touradj Ebrahimi, Karin Diserens
License	—
Citation / DOI	Unknown
Source links	OpenNeuro \| NeMAR \| Source URL

NM000231: eeg dataset, 8 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#