NM000190: eeg dataset, 10 subjects#

BNCI 2015-012 PASS2D P300 dataset

Access recordings and metadata through EEGDash.

Citation: Johannes Höhne, Martijn Schreuder, Benjamin Blankertz, Michael Tangermann (2011). BNCI 2015-012 PASS2D 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 NM000190

dataset = NM000190(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Filter by subject

dataset = NM000190(cache_dir="./data", subject="01")

Advanced query

dataset = NM000190(
    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{nm000190,
  title = {BNCI 2015-012 PASS2D P300 dataset},
  author = {Johannes Höhne and Martijn Schreuder and Benjamin Blankertz and Michael Tangermann},
}

About This Dataset#

BNCI 2015-012 PASS2D P300 dataset

BNCI 2015-012 PASS2D P300 dataset.

Dataset Overview

  • Code: BNCI2015-012

  • Paradigm: p300

  • DOI: 10.3389/fnins.2011.00099

View full README

BNCI 2015-012 PASS2D P300 dataset

BNCI 2015-012 PASS2D P300 dataset.

Dataset Overview

  • Code: BNCI2015-012

  • Paradigm: p300

  • DOI: 10.3389/fnins.2011.00099

  • Subjects: 10

  • Sessions per subject: 1

  • Events: Target=1, NonTarget=2

  • Trial interval: [0, 0.8] s

  • Runs per session: 2

  • Session IDs: session_1

  • File format: gdf

  • Data preprocessed: True

  • Contributing labs: Berlin Institute of Technology, Fraunhofer FIRST

Acquisition

  • Sampling rate: 250.0 Hz

  • Number of channels: 63

  • Channel types: eeg=63

  • Channel names: AF3, AF4, AF7, AF8, C1, C2, C3, C4, C5, C6, CP1, CP2, CP3, CP4, CP5, CP6, CPz, Cz, F1, F10, F2, F3, F4, F5, F6, F7, F8, F9, FC1, FC2, FC3, FC4, FC5, FC6, FCz, FT7, FT8, Fp1, Fp2, Fz, O1, O2, Oz, P1, P10, P2, P3, P4, P5, P6, P7, P8, P9, PO3, PO4, PO7, PO8, POz, Pz, T7, T8, TP7, TP8

  • Montage: 10-20

  • Hardware: Brain Products

  • Software: Matlab

  • Reference: nose

  • Sensor type: wet Ag/AgCl electrodes

  • Line frequency: 50.0 Hz

  • Online filters: 0.1-250 Hz analog bandpass, then 40 Hz lowpass

  • Cap manufacturer: EasyCap GmbH

  • Cap model: Fast’n Easy Cap

  • Electrode type: wet Ag/AgCl electrodes

  • Electrode material: Ag/AgCl

  • Auxiliary channels: EOG (1 ch)

Participants

  • Number of subjects: 10

  • Health status: patients

  • Clinical population: Healthy

  • Age: mean=25.1, min=21, max=34

  • Gender distribution: male=9, female=3

  • BCI experience: mostly naive

  • Species: human

Experimental Protocol

  • Paradigm: p300

  • Task type: auditory ERP speller

  • Number of classes: 2

  • Class labels: Target, NonTarget

  • Tasks: text spelling, counting task

  • Study design: Nine-class auditory ERP paradigm with predictive text entry system (PASS2D). Users focus attention on two-dimensional auditory stimuli varying in pitch (high/medium/low) and direction (left/middle/right) presented via headphones.

  • Study domain: communication

  • Feedback type: visual

  • Stimulus type: auditory tones

  • Stimulus modalities: auditory, visual

  • Primary modality: auditory

  • Synchronicity: synchronous

  • Mode: online

  • Training/test split: True

  • Instructions: Focus on target stimuli while ignoring all non-target stimuli. Minimize eye movements and muscle artifacts. Count targets during calibration. Spell sentences during online phase.

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

  • Stimulus frequencies: [708.0, 524.0, 380.0] Hz

  • Number of targets: 9

  • Number of repetitions: 15

  • Inter-stimulus interval: 125.0 ms

  • Stimulus onset asynchrony: 225.0 ms

Data Structure

  • Trials: 27

  • Trials context: total across all calibration runs (3 runs × 9 trials per run)

Preprocessing

  • Data state: filtered and downsampled

  • Preprocessing applied: True

  • Steps: analog bandpass filter, lowpass filter, downsampling, artifact rejection

  • Highpass filter: 0.1 Hz

  • Lowpass filter: 40.0 Hz

  • Bandpass filter: {‘low_cutoff_hz’: 0.1, ‘high_cutoff_hz’: 250.0}

  • Filter type: analog bandpass then digital lowpass

  • Artifact methods: threshold rejection

  • Re-reference: nose

  • Downsampled to: 100.0 Hz

  • Epoch window: [-0.15, 0.8]

  • Notes: Epochs with peak-to-peak voltage difference exceeding 100 μV in any channel were rejected during calibration. No artifact correction applied in online runs.

Signal Processing

  • Classifiers: FDA, Fisher discriminant analysis

  • Feature extraction: mean amplitude in discriminative intervals

  • Spatial filters: shrinkage regularization

Cross-Validation

  • Method: cross-validation

  • Evaluation type: within_session

Performance (Original Study)

  • Accuracy: 72.5%

  • Itr: 3.4 bits/min

  • Characters Per Minute: 0.8

  • Spelling Speed Chars Per Min: 0.8

BCI Application

  • Applications: speller, communication

  • Environment: laboratory

  • Online feedback: True

Tags

  • Pathology: Healthy

  • Modality: Auditory

  • Type: ERP, P300

Documentation

  • Description: A novel 9-class auditory ERP paradigm driving a predictive text entry system

  • DOI: 10.3389/fnins.2011.00099

  • Associated paper DOI: 10.3389/fnins.2011.00112

  • License: CC-BY-NC-ND-4.0

  • Investigators: Johannes Höhne, Martijn Schreuder, Benjamin Blankertz, Michael Tangermann

  • Senior author: Michael Tangermann

  • Contact: j.hoehne@tu-berlin.de

  • Institution: Berlin Institute of Technology

  • Department: Machine Learning Laboratory

  • Address: Franklinstr. 28/19, 10587 Berlin, Germany

  • Country: Germany

  • Repository: BNCI Horizon

  • Publication year: 2011

  • Keywords: brain–computer interface, BCI, auditory ERP, P300, N200, spatial auditory stimuli, T9, user-centered design

Abstract

Brain–computer interfaces (BCIs) based on event related potentials (ERPs) strive for offering communication pathways which are independent of muscle activity. While most visual ERP-based BCI paradigms require good control of the user’s gaze direction, auditory BCI paradigms overcome this restriction. The present work proposes a novel approach using auditory evoked potentials for the example of a multiclass text spelling application. To control the ERP speller, BCI users focus their attention to two-dimensional auditory stimuli that vary in both, pitch (high/medium/low) and direction (left/middle/right) and that are presented via headphones. The resulting nine different control signals are exploited to drive a predictive text entry system. It enables the user to spell a letter by a single nine-class decision plus two additional decisions to confirm a spelled word. This paradigm – called PASS2D – was investigated in an online study with 12 healthy participants. Users spelled with more than 0.8 characters per minute on average (3.4 bits/min) which makes PASS2D a competitive method. It could enrich the toolbox of existing ERP paradigms for BCI end users like people with amyotrophic lateral sclerosis disease in a late stage.

Methodology

Participants performed a single session lasting 3-4 hours consisting of calibration phase and online spelling task. Calibration: 3 runs (plus 1 practice run), each with 9 trials covering all 9 stimuli as targets. Each trial had 13-14 pseudo-random sequences of all 9 auditory stimuli (108 subtrials total, 12 target + 96 non-target). Online spelling: 2 runs spelling German sentences using T9-style predictive text system with 9-class decisions. Each trial consisted of 135 subtrials (15 iterations of 9 stimuli). Binary classification using linear FDA with shrinkage regularization on 2-4 amplitude values per channel from discriminative intervals (N200 at 230-300ms and P300 at 350+ ms). Multiclass decision based on one-sided t-test with unequal variances across 15 classifier outputs per key.

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 .. versionadded:: 1.2.0 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

NM000190

Title

BNCI 2015-012 PASS2D P300 dataset

Author (year)

Hohne2015

Canonical

BNCI2015

Importable as

NM000190, Hohne2015, BNCI2015

Year

2011

Authors

Johannes Höhne, Martijn Schreuder, Benjamin Blankertz, 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: 63

  • Sampling rate (Hz): 250.0

  • Duration (hours): 13.575294444444443

Tags

  • Pathology: Healthy

  • Modality: Auditory

  • Type: Attention

Files & format

  • Size on disk: 2.2 GB

  • File count: 20

  • Format: BIDS

License & citation

  • License: CC-BY-NC-ND-4.0

  • DOI: —

Provenance

API Reference#

Use the NM000190 class to access this dataset programmatically.

class eegdash.dataset.NM000190(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

BNCI 2015-012 PASS2D P300 dataset

Study:

nm000190 (NeMAR)

Author (year):

Hohne2015

Canonical:

—

Also importable as: NM000190, Hohne2015.

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/nm000190 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000190

Examples

>>> from eegdash.dataset import NM000190
>>> dataset = NM000190(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#