NM000198: eeg dataset, 13 subjects#

BNCI 2015-008 Center Speller P300 dataset

Access recordings and metadata through EEGDash.

Citation: M S Treder, N M Schmidt, B Blankertz (2011). BNCI 2015-008 Center Speller P300 dataset.

Modality: eeg Subjects: 13 Recordings: 26 License: CC-BY-NC-ND-4.0 Source: nemar

Metadata: Complete (90%)

Quickstart#

Install

pip install eegdash

Access the data

from eegdash.dataset import NM000198

dataset = NM000198(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Filter by subject

dataset = NM000198(cache_dir="./data", subject="01")

Advanced query

dataset = NM000198(
    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{nm000198,
  title = {BNCI 2015-008 Center Speller P300 dataset},
  author = {M S Treder and N M Schmidt and B Blankertz},
}

About This Dataset#

BNCI 2015-008 Center Speller P300 dataset

BNCI 2015-008 Center Speller P300 dataset.

Dataset Overview

  • Code: BNCI2015-008

  • Paradigm: p300

  • DOI: 10.1088/1741-2560/8/6/066003

View full README

BNCI 2015-008 Center Speller P300 dataset

BNCI 2015-008 Center Speller P300 dataset.

Dataset Overview

  • Code: BNCI2015-008

  • Paradigm: p300

  • DOI: 10.1088/1741-2560/8/6/066003

  • Subjects: 13

  • Sessions per subject: 1

  • Events: Target=1, NonTarget=2

  • Trial interval: [0, 1.0] s

  • Runs per session: 2

  • File format: gdf

  • Data preprocessed: True

Acquisition

  • Sampling rate: 250.0 Hz

  • Number of channels: 63

  • Channel types: eeg=63

  • Channel names: Fp2, AF3, AF4, Fz, F1, F2, F3, F4, F5, F6, F7, F8, F9, F10, FCz, FC1, FC2, FC3, FC4, FC5, FC6, T7, T8, Cz, C1, C2, C3, C4, C5, C6, TP7, TP8, CPz, CP1, CP2, CP3, CP4, CP5, CP6, Pz, P1, P2, P3, P4, P5, P6, P7, P8, P9, P10, POz, PO3, PO4, PO7, PO8, PO9, PO10, Oz, O1, O2, Iz, I1, I2

  • Montage: 10-10

  • Hardware: Brain Products actiCAP

  • Reference: left mastoid

  • Ground: forehead

  • Sensor type: active electrode

  • Line frequency: 50.0 Hz

  • Online filters: 0.016-250 Hz bandpass

  • Impedance threshold: 20.0 kOhm

  • Cap manufacturer: Brain Products

Participants

  • Number of subjects: 13

  • Health status: patients

  • Clinical population: Healthy

  • Age: mean=27.0, min=16.0, max=45.0

  • Gender distribution: male=8, female=5

  • Handedness: {‘right’: 12, ‘left’: 1}

  • BCI experience: naive

  • Species: human

Experimental Protocol

  • Paradigm: p300

  • Number of classes: 2

  • Class labels: Target, NonTarget

  • Trial duration: 30.0 s

  • Study design: Two-stage visual speller using covert spatial attention and non-spatial feature attention (color and form). Three speller variants tested: Hex-o-Spell (6 discs with size enhancement and unique colors), Cake Speller (6 triangular faces with unique colors), Center Speller (sequential presentation of 6 geometric shapes with unique colors and forms).

  • Feedback type: none

  • Stimulus type: visual_flash

  • Stimulus modalities: visual

  • Primary modality: visual

  • Synchronicity: synchronous

  • Mode: online

  • Training/test split: True

  • Instructions: Participants had to strictly fixate the center of the screen and covertly attend to the target symbol. They were instructed to silently count the number of intensifications of the target symbol.

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: 200.0 ms

Data Structure

  • Trials: 60 intensifications per stage (10 sequences × 6 elements)

  • Trials context: per_stage

Preprocessing

  • Data state: filtered

  • Preprocessing applied: True

  • Steps: downsampling, lowpass filter, baseline correction

  • Highpass filter: 0.016 Hz

  • Lowpass filter: 49.0 Hz

  • Bandpass filter: {‘low_cutoff_hz’: 0.016, ‘high_cutoff_hz’: 250.0}

  • Filter type: Chebyshev

  • Re-reference: linked mastoids

  • Downsampled to: 250.0 Hz

  • Epoch window: [-200.0, 800.0]

  • Notes: For offline ERP analysis: downsampled to 250 Hz, lowpass filtered below 49 Hz using Chebyshev filter (passbands/stopbands: 42/49 Hz). For online classification: downsampled to 100 Hz, no software filter applied. Baseline correction using -200 ms prestimulus interval.

Signal Processing

  • Classifiers: LDA, SLDA

  • Feature extraction: ERP components, P300, P3

  • Spatial filters: shrinkage covariance

Cross-Validation

  • Method: calibration-test split

  • Evaluation type: within_session

Performance (Original Study)

  • Accuracy: 92.0%

  • Hex O Spell Accuracy: 88.0

  • Cake Speller Accuracy: 90.0

  • Center Speller Accuracy: 97.0

  • Communication Rate Symbols Per Min: 2.3

BCI Application

  • Applications: speller, communication

  • Environment: laboratory

  • Online feedback: True

Tags

  • Pathology: Healthy

  • Modality: Visual

  • Type: ERP, P300

Documentation

  • DOI: 10.1088/1741-2560/8/6/066003

  • License: CC-BY-NC-ND-4.0

  • Investigators: M S Treder, N M Schmidt, B Blankertz

  • Institution: Berlin Institute of Technology

  • Department: Machine Learning Laboratory

  • Country: Germany

  • Repository: GitHub

  • Data URL: https://github.com/bbci/bbci_public/blob/master/doc/index.markdown

  • Publication year: 2011

  • Keywords: P300, ERP, BCI, speller, covert attention, feature attention, gaze-independent

References

Treder, M. S., Schmidt, N. M., & Blankertz, B. (2011). Gaze-independent brain-computer interfaces based on covert attention and feature attention. Journal of Neural Engineering, 8(6), 066003. https://doi.org/10.1088/1741-2560/8/6/066003 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

NM000198

Title

BNCI 2015-008 Center Speller P300 dataset

Author (year)

Treder2015_P300

Canonical

BNCI2015_P300, BNCI2015_008_P300, BNCI2015_008_CenterSpeller

Importable as

NM000198, Treder2015_P300, BNCI2015_P300, BNCI2015_008_P300, BNCI2015_008_CenterSpeller

Year

2011

Authors

M S Treder, N M Schmidt, B 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: 13

  • Recordings: 26

  • Tasks: 1

Channels & sampling rate

  • Channels: 63

  • Sampling rate (Hz): 250.0

  • Duration (hours): 19.37079333333333

Tags

  • Pathology: Healthy

  • Modality: Visual

  • Type: Attention

Files & format

  • Size on disk: 3.1 GB

  • File count: 26

  • Format: BIDS

License & citation

  • License: CC-BY-NC-ND-4.0

  • DOI: —

Provenance

API Reference#

Use the NM000198 class to access this dataset programmatically.

class eegdash.dataset.NM000198(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

BNCI 2015-008 Center Speller P300 dataset

Study:

nm000198 (NeMAR)

Author (year):

Treder2015_P300

Canonical:

BNCI2015_008_P300, BNCI2015_008_CenterSpeller

Also importable as: NM000198, Treder2015_P300, BNCI2015_008_P300, BNCI2015_008_CenterSpeller.

Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 13; recordings: 26; 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/nm000198 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000198

Examples

>>> from eegdash.dataset import NM000198
>>> dataset = NM000198(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#