NM000194: eeg dataset, 12 subjects#

Name: BNCI 2015-010 RSVP P300 dataset
Published: 2013-01-01
License: CC-BY-NC-ND-4.0

BNCI 2015-010 RSVP P300 dataset

Access recordings and metadata through EEGDash.

Citation: Laura Acqualagna, Benjamin Blankertz (2013). BNCI 2015-010 RSVP P300 dataset.

Modality: eeg Subjects: 12 Recordings: 24 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 NM000194

dataset = NM000194(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 = NM000194(cache_dir="./data", subject="01")

Advanced query

dataset = NM000194(
    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{nm000194,
  title = {BNCI 2015-010 RSVP P300 dataset},
  author = {Laura Acqualagna and Benjamin Blankertz},
}

About This Dataset#

BNCI 2015-010 RSVP P300 dataset

BNCI 2015-010 RSVP P300 dataset.

Dataset Overview

Code: BNCI2015-010
Paradigm: p300
DOI: 10.1016/j.clinph.2012.12.050

View full README

BNCI 2015-010 RSVP P300 dataset

BNCI 2015-010 RSVP P300 dataset.

Dataset Overview

Code: BNCI2015-010
Paradigm: p300
DOI: 10.1016/j.clinph.2012.12.050
Subjects: 12
Sessions per subject: 1
Events: Target=1, NonTarget=2
Trial interval: [0, 0.8] s
Runs per session: 2
Session IDs: calibration, copy-spelling, free-spelling
File format: EEG
Data preprocessed: True

Acquisition

Sampling rate: 200.0 Hz
Number of channels: 63
Channel types: eeg=63
Channel names: Fp1, Fp2, AF3, AF4, Fz, F1, F2, F3, F4, F5, F6, F7, F8, F9, F10, FCz, FC1, FC2, FC3, FC4, FC5, FC6, FT7, FT8, Cz, C1, C2, C3, C4, C5, C6, T7, T8, CPz, CP1, CP2, CP3, CP4, CP5, CP6, TP7, TP8, Pz, P1, P2, P3, P4, P5, P6, P7, P8, P9, P10, POz, PO3, PO4, PO7, PO8, PO9, PO10, Oz, O1, O2
Montage: 10-20
Hardware: BrainAmp amplifiers
Software: Python with Pyff framework
Reference: left mastoid
Sensor type: active electrode
Line frequency: 50.0 Hz
Online filters: lowpass Chebyshev filter up to 40 Hz
Impedance threshold: 10.0 kOhm
Cap manufacturer: Brain Products
Cap model: actiCap
Electrode type: active electrode

Participants

Number of subjects: 12
Health status: patients
Clinical population: Healthy
Age: mean=29.17, std=8.4, min=24, max=55
Gender distribution: male=6, female=6
Handedness: all right-handed
BCI experience: mixed
Species: human

Experimental Protocol

Paradigm: p300
Task type: spelling
Number of classes: 2
Class labels: Target, NonTarget
Trial duration: 46.5 s
Study design: RSVP (Rapid Serial Visual Presentation) BCI speller where 30 symbols are presented one-by-one in random order at the center of the screen. Three conditions tested: NoColor 116ms SOA, Color 116ms SOA, and Color 83ms SOA. Colors used to facilitate discrimination.
Feedback type: visual
Stimulus type: RSVP letters
Stimulus modalities: visual
Primary modality: visual
Synchronicity: synchronous
Mode: online
Training/test split: True
Instructions: Participants fixate center of screen, concentrate on target letter, silently count its occurrences. Avoid blinking during visual presentation.

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: 30
Number of repetitions: 10
Stimulus onset asynchrony: 116.0 ms

Data Structure

Trials: 10 sequences of 30 symbols
Blocks per session: 3
Trials context: per sequence

Preprocessing

Data state: filtered
Preprocessing applied: True
Steps: lowpass filter, downsampling, baseline correction, artifact rejection
Lowpass filter: 40.0 Hz
Filter type: Chebyshev
Filter order: passband up to 40 Hz, stopband starting at 49 Hz
Artifact methods: min-max criterion for eye movement rejection (75 µV on F9, Fz, F10, AF3, AF4), broadband power rejection (5-40 Hz)
Re-reference: linked mastoids (offline)
Downsampled to: 200.0 Hz
Epoch window: [-0.1, 1.2]
Notes: Baseline correction on pre-stimulus interval (116ms for 116ms SOA, 83/2ms for 83ms SOA). Non-target epochs excluded if 3 preceding or following symbols were targets.

Signal Processing

Classifiers: LDA with shrinkage
Feature extraction: spatio-temporal features, averaged voltages within time windows
Frequency bands: alpha=[7, 13] Hz
Spatial filters: 55 channels used for classification (all except Fp1,2, AF3,4, F9,10, FT7,8)

Cross-Validation

Method: calibration/test split
Evaluation type: within_session

Performance (Original Study)

Accuracy: 94.8%
Mean Spelling Rate Symb Per Min: 1.43
Trial Duration 116Ms Soa S: 46.5
Trial Duration 83Ms Soa S: 36.6

BCI Application

Applications: speller, communication
Environment: laboratory
Online feedback: True

Tags

Pathology: Healthy
Modality: Visual
Type: ERP

Documentation

DOI: 10.1016/j.clinph.2012.12.050
License: CC-BY-NC-ND-4.0
Investigators: Laura Acqualagna, Benjamin Blankertz
Senior author: Benjamin Blankertz
Contact: laura.acqualagna@tu-berlin.de; benjamin.blankertz@tu-berlin.de
Institution: Berlin Institute of Technology
Department: Machine Learning Laboratory; Neurotechnology Group
Country: Germany
Repository: BNCI Horizon
Publication year: 2013
Funding: BMBF Grant; Grant Nos s; Grant No. MU MU; DFG Grant
Ethics approval: Study performed in accordance with the declaration of Helsinki
Keywords: Brain Computer Interfaces, RSVP, ERPs, Speller, P300, N2, gaze-independent

Abstract

A Brain Computer Interface (BCI) speller using rapid serial visual presentation (RSVP) paradigm for gaze-independent mental typewriting. Twelve healthy participants successfully operated the RSVP speller with mean online spelling rate of 1.43 symb/min and mean symbol selection accuracy of 94.8%. The RSVP speller does not require gaze shifts and can be operated by non-spatial visual attention, making it suitable for patients with impaired oculo-motor control.

Methodology

Three experimental conditions tested (NoColor 116ms, Color 116ms, Color 83ms SOA). Each condition included calibration, copy-spelling, and free-spelling phases. Vocabulary of 30 symbols presented one-by-one at screen center in pseudo-random order. EEG recorded at 1000 Hz with 63 channels, downsampled to 200 Hz for ERP analysis. Classification using LDA with shrinkage on spatio-temporal features from 5 individually selected time windows. Symbol selection based on averaged classifier output across 10 sequences.

References

Acqualagna, L., & Blankertz, B. (2013). Gaze-independent BCI-spelling using rapid serial visual presentation (RSVP). Clinical Neurophysiology, 124(5), 901-908. https://doi.org/10.1016/j.clinph.2012.12.050 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	`NM000194`
Title	BNCI 2015-010 RSVP P300 dataset
Author (year)	`Acqualagna2015`
Canonical	—
Importable as	`NM000194`, `Acqualagna2015`
Year	2013
Authors	Laura Acqualagna, Benjamin Blankertz
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: 12
Recordings: 24
Tasks: 1

Channels & sampling rate

Channels: 63 (22), 61 (2)
Sampling rate (Hz): 200.0
Duration (hours): 16.163227777777777

Electrode Layout#

Electrode layout — EEG · 63 sensors — 63 channels

Dataset Statistics#

Age distribution (n=12, range 29–29 yr)

25

Channel counts (ch)

6163

Sampling frequencies: 200.0 Hz (n=24 recordings)

Total recording duration: 16 h 9 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 NM000194 class to access this dataset programmatically.

class eegdash.dataset.NM000194(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

BNCI 2015-010 RSVP P300 dataset

Study:: nm000194 (NeMAR)
Author (year):: Acqualagna2015
Canonical:: —

Also importable as: NM000194, Acqualagna2015.

Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 12; recordings: 24; 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/nm000194 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000194

Examples

>>> from eegdash.dataset import NM000194
>>> dataset = NM000194(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)