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#

Get Started

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)

Query & Filter

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'])

Cite This Dataset

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) NeuroTechX/moabb

Dataset Information#

Dataset ID	`NM000219`
Title	BNCI 2020-002 Attention Shift (Covert Spatial Attention) dataset
Author (year)	`Reichert2020`
Canonical	—
Importable as	`NM000219`, `Reichert2020`
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

Electrode Layout#

Electrode layout — EEG · 26 sensors — 26 channels

Dataset Statistics#

Age distribution (n=18, range 18–38 yr)

1520253035

Sex distribution

10

8

Female Male Total: 18

Channel counts: 30 ch (n=18 recordings)

Sampling frequencies: 250.0 Hz (n=18 recordings)

Total recording duration: 13 h 13 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 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:: —

Also importable as: NM000219, Reichert2020.

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: 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)