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#

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)

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'])

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) https://github.com/NeuroTechX/moabb

Dataset Information#

Dataset ID

NM000231

Title

P300 dataset from Hoffmann et al 2008

Author (year)

Hoffmann2008

Canonical

EPFLP300, EPFL_P300, EPFLP300Dataset

Importable as

NM000231, Hoffmann2008, EPFLP300, EPFL_P300, EPFLP300Dataset

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

Tags

  • Pathology: Other

  • Modality: Visual

  • Type: Attention

Files & format

  • Size on disk: 1.9 GB

  • File count: 192

  • Format: BIDS

License & citation

  • License: See source

  • DOI: —

Provenance

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:

EPFLP300, EPFL_P300, EPFLP300Dataset

Also importable as: NM000231, Hoffmann2008, EPFLP300, EPFL_P300, EPFLP300Dataset.

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, 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#