NM000189: eeg dataset, 10 subjects#

BNCI 2015-003 P300 dataset

Access recordings and metadata through EEGDash.

Citation: Martijn Schreuder, Thomas Rost, Michael Tangermann (2011). BNCI 2015-003 P300 dataset.

Modality: eeg Subjects: 10 Recordings: 20 License: CC-BY-NC-ND-4.0 Source: nemar

Metadata: Complete (90%)

Quickstart#

Install

pip install eegdash

Access the data

from eegdash.dataset import NM000189

dataset = NM000189(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Filter by subject

dataset = NM000189(cache_dir="./data", subject="01")

Advanced query

dataset = NM000189(
    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{nm000189,
  title = {BNCI 2015-003 P300 dataset},
  author = {Martijn Schreuder and Thomas Rost and Michael Tangermann},
}

About This Dataset#

BNCI 2015-003 P300 dataset

BNCI 2015-003 P300 dataset.

Dataset Overview

  • Code: BNCI2015-003

  • Paradigm: p300

  • DOI: 10.1016/j.neulet.2009.06.045

View full README

BNCI 2015-003 P300 dataset

BNCI 2015-003 P300 dataset.

Dataset Overview

  • Code: BNCI2015-003

  • Paradigm: p300

  • DOI: 10.1016/j.neulet.2009.06.045

  • Subjects: 10

  • Sessions per subject: 1

  • Events: Target=2, NonTarget=1

  • Trial interval: [0, 0.8] s

  • Runs per session: 2

  • Session IDs: Session 1, Session 2

  • File format: gdf

  • Data preprocessed: True

  • Number of contributing labs: 1

Acquisition

  • Sampling rate: 256.0 Hz

  • Number of channels: 8

  • Channel types: eeg=8

  • Channel names: Fz, Cz, P3, Pz, P4, PO7, Oz, PO8

  • Montage: standard_1005

  • Hardware: BrainAmp

  • Software: Matlab

  • Reference: nose

  • Sensor type: Ag/AgCl electrodes

  • Line frequency: 50.0 Hz

  • Online filters: hardware analog band-pass filter between 0.1 and 250 Hz

  • Impedance threshold: 15.0 kOhm

  • Cap manufacturer: Brain Products

  • Electrode type: Ag/AgCl

  • Electrode material: silver/silver chloride

  • Auxiliary channels: EOG (2 ch, bipolar)

Participants

  • Number of subjects: 10

  • Health status: patients

  • Clinical population: Healthy

  • Age: mean=34.1, std=11.4, min=20, max=57

  • BCI experience: naive

  • Species: human

Experimental Protocol

  • Paradigm: p300

  • Task type: auditory_oddball

  • Number of classes: 2

  • Class labels: Target, NonTarget

  • Tasks: spelling, auditory_attention

  • Study design: Auditory Multi-class Spatial ERP (AMUSE) paradigm using spatial auditory cues from six speaker locations in azimuth plane. Two-step hex-o-spell like interface for character selection. Subjects mentally count target stimuli from one of six spatial directions.

  • Study domain: communication

  • Feedback type: auditory

  • Stimulus type: spatial_auditory

  • Stimulus modalities: auditory

  • Primary modality: auditory

  • Synchronicity: synchronous

  • Mode: online

  • Training/test split: True

  • Instructions: Focus attention to one target direction and mentally count the number of appearances

  • Stimulus presentation: soa_ms=175, stimulus_duration_ms=40, stimulus_intensity_db=58, speaker_arrangement=6 speakers at ear height, evenly distributed in circle with 60° distance, radius 65 cm

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

  • Stimulus onset asynchrony: 175.0 ms

Data Structure

  • Trials: 48

  • Trials per class: calibration_per_direction=8

  • Trials context: calibration_phase

Preprocessing

  • Data state: filtered

  • Preprocessing applied: True

  • Steps: low-pass filter, downsampling, baselining

  • Highpass filter: 0.1 Hz

  • Lowpass filter: 40.0 Hz

  • Bandpass filter: {‘low_cutoff_hz’: 0.1, ‘high_cutoff_hz’: 40.0}

  • Filter type: analog hardware filter for acquisition; low-pass for online

  • Artifact methods: variance criterium, peak-to-peak difference criterium

  • Re-reference: nose

  • Downsampled to: 100.0 Hz

  • Epoch window: [-0.15, None]

  • Notes: For online use signal was low-pass filtered below 40 Hz and downsampled to 100 Hz. Data baselined using 150 ms pre-stimulus data as reference.

Signal Processing

  • Classifiers: LDA, linear binary classifier

  • Feature extraction: spatio-temporal features, r2 coefficient, interval averaging

  • Spatial filters: shrinkage regularization (Ledoit-Wolf)

Cross-Validation

  • Method: online

  • Evaluation type: online

Performance (Original Study)

  • Accuracy: 77.4%

  • Itr: 2.84 bits/min

  • Char Per Min Session1: 0.59

  • Char Per Min Session2 Max: 1.41

  • Char Per Min Session2 Avg: 0.94

  • Itr Session2 Avg: 5.26

  • Itr Session2 Max: 7.55

  • Success Rate Session1: 76.0

BCI Application

  • Applications: speller, communication

  • Environment: laboratory

  • Online feedback: True

Tags

  • Pathology: Healthy

  • Modality: Auditory

  • Type: ERP, P300

Documentation

  • Description: Auditory BCI speller using spatial cues (AMUSE paradigm) allowing purely auditory communication interface

  • DOI: 10.1016/j.neulet.2009.06.045

  • Associated paper DOI: 10.3389/fnins.2011.00112

  • License: CC-BY-NC-ND-4.0

  • Investigators: Martijn Schreuder, Thomas Rost, Michael Tangermann

  • Senior author: Michael Tangermann

  • Contact: schreuder@tu-berlin.de

  • Institution: Berlin Institute of Technology

  • Department: Machine Learning Laboratory

  • Address: Machine Learning Laboratory, Berlin Institute of Technology, FR6-9, Franklinstraße 28/29, 10587 Berlin, Germany

  • Country: Germany

  • Repository: BNCI Horizon

  • Publication year: 2011

  • Funding: European ICT Programme Project FP7-224631; European ICT Programme Project FP7-216886; Deutsche Forschungsgemeinschaft (DFG MU 987/3-2); Bundesministerium fur Bildung und Forschung (BMBF FKZ 01IB001A, 01GQ0850); FP7-ICT PASCAL2 Network of Excellence ICT-216886

  • Ethics approval: Ethics Committee of the Charité University Hospital

  • Acknowledgements: Thomas Denck, David List and Larissa Queda for help with experiments. Klaus-Robert Müller and Benjamin Blankertz for fruitful discussions.

  • Keywords: brain-computer interface, directional hearing, auditory event-related potentials, P300, N200, dynamic subtrials

External Links

Abstract

This online study introduces an auditory spelling interface that eliminates the necessity for visual representation. In up to two sessions, a group of healthy subjects (N=21) was asked to use a text entry application, utilizing the spatial cues of the AMUSE paradigm (Auditory Multi-class Spatial ERP). The speller relies on the auditory sense both for stimulation and the core feedback. Without prior BCI experience, 76% of the participants were able to write a full sentence during the first session. By exploiting the advantages of a newly introduced dynamic stopping method, a maximum writing speed of 1.41 char/min (7.55 bits/min) could be reached during the second session (average: 0.94 char/min, 5.26 bits/min).

Methodology

Participants surrounded by six speakers at ear height in circle (60° spacing, 65 cm radius). Each direction associated with unique combination of tone (base frequency + harmonics) and band-pass filtered noise. Two-step hex-o-spell interface for character selection. Session 1: calibration (48 trials, 8 per direction, 15 iterations each) followed by online spelling with 15 fixed iterations. Session 2: calibration followed by online spelling with dynamic stopping method (4-15 iterations). Spatio-temporal feature extraction using r2 coefficient and interval selection (2-4 intervals for early and late components, 112-224 features total). Linear binary classifier with shrinkage regularization (Ledoit-Wolf). Decision making based on median classifier scores across iterations.

References

Schreuder, M., Rost, T., & Tangermann, M. (2011). Listen, you are writing! Speeding up online spelling with a dynamic auditory BCI. Frontiers in neuroscience, 5, 112. https://doi.org/10.3389/fnins.2011.00112 Notes .. note:: BNCI2015_003 was previously named BNCI2015003. BNCI2015003 will be removed in version 1.1. .. versionadded:: 0.4.0 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

NM000189

Title

BNCI 2015-003 P300 dataset

Author (year)

Schreuder2015_P300

Canonical

BNCI2015_P300, BNCI2015_003_P300, BNCI2015_003_AMUSE

Importable as

NM000189, Schreuder2015_P300, BNCI2015_P300, BNCI2015_003_P300, BNCI2015_003_AMUSE

Year

2011

Authors

Martijn Schreuder, Thomas Rost, Michael Tangermann

License

CC-BY-NC-ND-4.0

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: 10

  • Recordings: 20

  • Tasks: 1

Channels & sampling rate

  • Channels: 8

  • Sampling rate (Hz): 256.0

  • Duration (hours): 0.9342003038194444

Tags

  • Pathology: Healthy

  • Modality: Auditory

  • Type: Attention

Files & format

  • Size on disk: 21.8 MB

  • File count: 20

  • Format: BIDS

License & citation

  • License: CC-BY-NC-ND-4.0

  • DOI: —

Provenance

API Reference#

Use the NM000189 class to access this dataset programmatically.

class eegdash.dataset.NM000189(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

BNCI 2015-003 P300 dataset

Study:

nm000189 (NeMAR)

Author (year):

Schreuder2015_P300

Canonical:

BNCI2015_P300, BNCI2015_003_P300, BNCI2015_003_AMUSE

Also importable as: NM000189, Schreuder2015_P300, BNCI2015_P300, BNCI2015_003_P300, BNCI2015_003_AMUSE.

Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 10; recordings: 20; 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/nm000189 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000189

Examples

>>> from eegdash.dataset import NM000189
>>> dataset = NM000189(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#