NM000219: eeg dataset, 18 subjects#

BNCI 2020-002 Attention Shift (Covert Spatial Attention) dataset

Access recordings and metadata through EEGDash.

Citation: Christoph Reichert, Igor Fabian Tellez Ceja, Catherine M. Sweeney-Reed, Hans-Jochen Heinze, Hermann Hinrichs, Stefan Dürschmid (2020). BNCI 2020-002 Attention Shift (Covert Spatial Attention) dataset.

Modality: eeg Subjects: 18 Recordings: 18 License: CC-BY-4.0 Source: nemar

Metadata: Complete (90%)

Quickstart#

Install

pip install eegdash

Access the data

from eegdash.dataset import NM000219

dataset = NM000219(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Filter by subject

dataset = NM000219(cache_dir="./data", subject="01")

Advanced query

dataset = NM000219(
    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{nm000219,
  title = {BNCI 2020-002 Attention Shift (Covert Spatial Attention) dataset},
  author = {Christoph Reichert and Igor Fabian Tellez Ceja and Catherine M. Sweeney-Reed and Hans-Jochen Heinze and Hermann Hinrichs and Stefan Dürschmid},
}

About This Dataset#

BNCI 2020-002 Attention Shift (Covert Spatial Attention) dataset

BNCI 2020-002 Attention Shift (Covert Spatial Attention) dataset.

Dataset Overview

  • Code: BNCI2020-002

  • Paradigm: p300

  • DOI: 10.3389/fnins.2020.591777

View full README

BNCI 2020-002 Attention Shift (Covert Spatial Attention) dataset

BNCI 2020-002 Attention Shift (Covert Spatial Attention) dataset.

Dataset Overview

  • Code: BNCI2020-002

  • Paradigm: p300

  • DOI: 10.3389/fnins.2020.591777

  • Subjects: 18

  • Sessions per subject: 1

  • Events: NonTarget=1, Target=2

  • Trial interval: [0, 16] s

  • File format: MAT

Acquisition

  • Sampling rate: 250.0 Hz

  • Number of channels: 30

  • Channel types: eeg=30, eog=2

  • Channel names: C3, C4, CP1, CP2, Cz, F3, F4, F7, F8, FC1, FC2, Fp1, Fp2, Fz, HEOG, IZ, LMAST, O10, O9, Oz, P3, P4, P7, P8, PO3, PO4, PO7, PO8, Pz, T7, T8, VEOG

  • Montage: extended 10-20

  • Hardware: BrainAmp DC Amplifier

  • Reference: right mastoid

  • Sensor type: Ag/AgCl electrodes

  • Line frequency: 50.0 Hz

  • Online filters: 0.1 Hz highpass

  • Cap manufacturer: Brain Products GmbH

  • Auxiliary channels: EOG (2 ch, horizontal, vertical)

Participants

  • Number of subjects: 18

  • Health status: healthy

  • Age: mean=27.0, min=19.0, max=38.0

  • Gender distribution: male=8, female=10

  • Species: human

Experimental Protocol

  • Paradigm: p300

  • Task type: binary decision

  • Number of classes: 2

  • Class labels: NonTarget, Target

  • Feedback type: visual (yes/no text)

  • Stimulus type: colored crosses (green + and red x)

  • Stimulus modalities: visual

  • Primary modality: visual

  • Synchronicity: synchronous

  • Mode: online

  • Training/test split: True

  • Instructions: Respond to yes/no questions by shifting attention to green cross (yes) or red cross (no) while maintaining central gaze fixation

  • Stimulus presentation: duration_ms=250, soa_ms=850 (jittered by 0-250 ms), stimuli_per_trial=10

HED Event Annotations

Schema: HED 8.4.0 | Browse: https://www.hedtags.org/hed-schema-browser

NonTarget

     ├─ Sensory-event
     ├─ Experimental-stimulus
     ├─ Visual-presentation
     └─ Non-target

Target

├─ Sensory-event
├─ Experimental-stimulus
├─ Visual-presentation
└─ Target

Paradigm-Specific Parameters

  • Detected paradigm: p300

  • Number of targets: 2

  • Number of repetitions: 10

  • Stimulus onset asynchrony: 850.0 ms

Data Structure

  • Trials: 24

  • Blocks per session: 7

  • Trials context: per_block

Preprocessing

  • Data state: raw

  • Preprocessing applied: False

  • Steps: re-referenced to average of left and right mastoid, 4th order zero-phase IIR Butterworth bandpass filter (1.0-12.5 Hz), resampled to 50 Hz, epoched from stimulus onset to 750 ms after

  • Highpass filter: 1.0 Hz

  • Lowpass filter: 12.5 Hz

  • Bandpass filter: [1.0, 12.5]

  • Filter type: Butterworth IIR

  • Filter order: 4

  • Re-reference: average of left and right mastoid

  • Downsampled to: 50.0 Hz

  • Epoch window: [0.0, 0.75]

Signal Processing

  • Classifiers: Canonical Correlation Analysis (CCA)

  • Feature extraction: N2pc, ERP, Canonical difference waves

  • Spatial filters: CCA spatial filters

Cross-Validation

  • Method: leave-one-out cross-validation (LOOCV)

  • Evaluation type: within_subject

Performance (Original Study)

  • Accuracy: 88.5%

  • Itr: 3.02 bits/min

  • Std Accuracy: 7.8

  • Min Accuracy: 70.8

  • Max Accuracy: 90.3

BCI Application

  • Applications: communication, binary decision

  • Environment: laboratory

  • Online feedback: True

Tags

  • Pathology: Healthy

  • Modality: Visual

  • Type: Attention

Documentation

  • Description: Gaze-independent brain-computer interface based on covert spatial attention shifts for binary (yes/no) communication

  • DOI: 10.3389/fnins.2020.591777

  • Associated paper DOI: 10.3389/fnins.2020.591777

  • License: CC-BY-4.0

  • Investigators: Christoph Reichert, Igor Fabian Tellez Ceja, Catherine M. Sweeney-Reed, Hans-Jochen Heinze, Hermann Hinrichs, Stefan Dürschmid

  • Senior author: Stefan Dürschmid

  • Contact: christoph.reichert@lin-magdeburg.de

  • Institution: Leibniz Institute for Neurobiology

  • Department: Department of Behavioral Neurology

  • Address: Magdeburg, Germany

  • Country: Germany

  • Repository: BNCI Horizon

  • Data URL: http://bnci-horizon-2020.eu/database/data-sets

  • Publication year: 2020

  • Funding: German Ministry of Education and Research (BMBF) within the Research Campus STIMULATE under grant number 13GW0095D

  • Ethics approval: Ethics Committee of the Otto-von-Guericke University, Magdeburg

  • Keywords: visual spatial attention, brain-computer interface, stimulus features, N2pc, canonical correlation analysis, gaze-independent, BCI

References

Reichert, C., Tellez-Ceja, I. F., Schwenker, F., Rusnac, A.-L., Curio, G., Aust, L., & Hinrichs, H. (2020). Impact of Stimulus Features on the Performance of a Gaze-Independent Brain-Computer Interface Based on Covert Spatial Attention Shifts. Frontiers in Neuroscience, 14, 591777. https://doi.org/10.3389/fnins.2020.591777 Notes .. versionadded:: 1.3.0 This dataset uses a covert spatial attention paradigm with N2pc ERP detection, which is different from traditional P300 or motor imagery paradigms. The paradigm is designed for gaze-independent BCI control, making it suitable for users who cannot control eye movements. See Also BNCI2015_009 : AMUSE auditory spatial P300 paradigm BNCI2015_010 : RSVP visual P300 paradigm Examples

>> from moabb.datasets import BNCI2020_002 >>> dataset = BNCI2020_002() >>> dataset.subject_list [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18]

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

NM000219

Title

BNCI 2020-002 Attention Shift (Covert Spatial Attention) dataset

Author (year)

Reichert2020

Canonical

BNCI2020, BNCI2020_002_AttentionShift, BNCI2020_002_CovertSpatialAttention

Importable as

NM000219, Reichert2020, BNCI2020, BNCI2020_002_AttentionShift, BNCI2020_002_CovertSpatialAttention

Year

2020

Authors

Christoph Reichert, Igor Fabian Tellez Ceja, Catherine M. Sweeney-Reed, Hans-Jochen Heinze, Hermann Hinrichs, Stefan Dürschmid

License

CC-BY-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: 18

  • Recordings: 18

  • Tasks: 1

Channels & sampling rate

  • Channels: 30

  • Sampling rate (Hz): 250.0

  • Duration (hours): 13.226646666666667

Tags

  • Pathology: Healthy

  • Modality: Visual

  • Type: Attention

Files & format

  • Size on disk: 1023.6 MB

  • File count: 18

  • Format: BIDS

License & citation

  • License: CC-BY-4.0

  • DOI: —

Provenance

API Reference#

Use the NM000219 class to access this dataset programmatically.

class eegdash.dataset.NM000219(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

BNCI 2020-002 Attention Shift (Covert Spatial Attention) dataset

Study:

nm000219 (NeMAR)

Author (year):

Reichert2020

Canonical:

BNCI2020, BNCI2020_002_AttentionShift, BNCI2020_002_CovertSpatialAttention

Also importable as: NM000219, Reichert2020, BNCI2020, BNCI2020_002_AttentionShift, BNCI2020_002_CovertSpatialAttention.

Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 18; recordings: 18; 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/nm000219 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000219

Examples

>>> from eegdash.dataset import NM000219
>>> dataset = NM000219(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#