NM000190: eeg dataset, 10 subjects#

Name: BNCI 2015-012 PASS2D P300 dataset
Published: 2011-01-01
License: CC-BY-NC-ND-4.0

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#

Get Started

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)

Query & Filter

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'])

Cite This Dataset

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) NeuroTechX/moabb

Dataset Information#

Dataset ID	`NM000190`
Title	BNCI 2015-012 PASS2D P300 dataset
Author (year)	`Hohne2015`
Canonical	—
Importable as	`NM000190`, `Hohne2015`
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

Electrode Layout#

Electrode layout — EEG · 63 sensors — 63 channels

Dataset Statistics#

Age distribution (n=10, range 21–34 yr)

202530

Sex distribution

8

Male Total: 8

Channel counts: 63 ch (n=20 recordings)

Sampling frequencies: 250.0 Hz (n=20 recordings)

Total recording duration: 13 h 34 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 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: 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)