NM000231: eeg dataset, 8 subjects#

P300 dataset from Hoffmann et al 2008

Access recordings and metadata through EEGDash.

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

Modality: eeg Subjects: 8 Recordings: 192 License: — Source: nemar

Metadata: Good (80%)

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},
}

About This Dataset#

P300 dataset from Hoffmann et al 2008

P300 dataset from Hoffmann et al 2008.

Dataset Overview

Code: EPFLP300
Paradigm: p300
DOI: 10.1016/j.jneumeth.2007.03.005

View full README

P300 dataset from Hoffmann et al 2008

P300 dataset from Hoffmann et al 2008.

Dataset Overview

Code: EPFLP300
Paradigm: p300
DOI: 10.1016/j.jneumeth.2007.03.005
Subjects: 8
Sessions per subject: 4
Events: Target=2, NonTarget=1
Trial interval: [0, 1] s
Runs per session: 6
File format: MATLAB

Acquisition

Sampling rate: 2048.0 Hz
Number of channels: 32
Channel types: eeg=32, misc=2
Channel names: AF3, AF4, C3, C4, CP1, CP2, CP5, CP6, Cz, F3, F4, F7, F8, FC1, FC2, FC5, FC6, Fp1, Fp2, Fz, MA1, MA2, O1, O2, Oz, P3, P4, P7, P8, PO3, PO4, Pz, T7, T8
Montage: standard_1020
Hardware: Biosemi ActiveTwo
Sensor type: active
Line frequency: 50.0 Hz

Participants

Number of subjects: 8
Health status: mixed
Clinical population: 4 disabled (cerebral palsy, multiple sclerosis, late-stage amyotrophic lateral sclerosis, traumatic brain and spinal-cord injury C4 level), 4 able-bodied
Age: mean=38.4, min=30, max=56
Gender distribution: male=7, female=1
BCI experience: no training required
Species: human

Experimental Protocol

Paradigm: p300
Number of classes: 2
Class labels: Target, NonTarget
Trial duration: 1.0 s
Study design: Subjects counted silently how often a prescribed image (one of six: television, telephone, lamp, door, window, radio) was flashed while images were flashed in random sequences
Feedback type: none
Stimulus type: image_flash
Stimulus modalities: visual
Primary modality: visual
Mode: offline
Instructions: Subjects were asked to count silently how often a prescribed image was flashed
Stimulus presentation: flash_duration=100ms, isi=400ms, display=six images (television, telephone, lamp, door, window, radio)

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: 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

Dataset Information#

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

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: 8
Recordings: 192
Tasks: 1

Channels & sampling rate

Channels: 32
Sampling rate (Hz): 2048.0
Duration (hours): 2.9408072916666668

Electrode Layout#

Electrode layout — EEG · 32 sensors — 32 channels

Dataset Statistics#

Age distribution (n=8, range 30–56 yr)

3035455055

Channel counts: 32 ch (n=192 recordings)

Sampling frequencies: 2048.0 Hz (n=192 recordings)

Total recording duration: 2 h 56 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 NM000231 class to access this dataset programmatically.

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

Bases: EEGDashDataset

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)