NM000168: eeg dataset, 6 subjects

BNCI 2015-013 Error-Related Potentials dataset

Access recordings and metadata through EEGDash.

Citation: Ricardo Chavarriaga, José del R. Millán (2010). BNCI 2015-013 Error-Related Potentials dataset.

Modality: eeg Subjects: 6 Recordings: 120 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 NM000168

dataset = NM000168(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 = NM000168(cache_dir="./data", subject="01")

Advanced query

dataset = NM000168(
    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{nm000168,
  title = {BNCI 2015-013 Error-Related Potentials dataset},
  author = {Ricardo Chavarriaga and José del R. Millán},
}

About This Dataset

BNCI 2015-013 Error-Related Potentials dataset

BNCI 2015-013 Error-Related Potentials dataset.

Dataset Overview

• Code: BNCI2015-013

• Paradigm: p300

• DOI: 10.1109/TNSRE.2010.2053387

View full README

BNCI 2015-013 Error-Related Potentials dataset

BNCI 2015-013 Error-Related Potentials dataset.

Dataset Overview

• Code: BNCI2015-013

• Paradigm: p300

• DOI: 10.1109/TNSRE.2010.2053387

• Subjects: 6

• Sessions per subject: 20

• Events: Target=1, NonTarget=2

• Trial interval: [0, 0.6] s

• File format: matlab

Acquisition

• Sampling rate: 512.0 Hz

• Number of channels: 64

• Channel types: eeg=64

• Channel names: Fp1, AF7, AF3, F1, F3, F5, F7, FT7, FC5, FC3, FC1, C1, C3, C5, T7, TP7, CP5, CP3, CP1, P1, P3, P5, P7, P9, PO7, PO3, O1, Iz, Oz, POz, Pz, CPz, Fpz, Fp2, AF8, AF4, AFz, Fz, F2, F4, F6, F8, FT8, FC6, FC4, FC2, FCz, Cz, C2, C4, C6, T8, TP8, CP6, CP4, CP2, P2, P4, P6, P8, P10, PO8, PO4, O2

• Montage: standard_1020

• Hardware: Biosemi ActiveTwo

• Sensor type: active

• Line frequency: 50.0 Hz

Participants

• Number of subjects: 6

• Health status: patients

• Clinical population: Healthy

• Age: mean=27.83, std=2.23

• Gender distribution: male=5, female=1

• Handedness: not reported

• BCI experience: not reported

• Species: human

Experimental Protocol

• Paradigm: p300

• Task type: monitoring

• Number of classes: 2

• Class labels: Target, NonTarget

• Trial duration: 2.0 s

• Study design: Error-related potential (ErrP) monitoring task where subjects observe a cursor moving towards a target. The cursor moves autonomously with 20% or 40% error probability. Subjects monitor performance without control.

• Feedback type: visual

• Stimulus type: cursor_movement

• Stimulus modalities: visual

• Primary modality: visual

• Synchronicity: synchronous

• Mode: offline

• Training/test split: True

• Instructions: Subjects seat in front of a computer screen and monitor a moving cursor (green square) and target location (blue for left, red for right). No control over cursor movement, only assess whether it performs properly. Fixate center of screen.

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

Data Structure

• Trials: ~50 trials per block, ~64 trials per block for error_prob=0.20

• Blocks per session: 10

• Block duration: 180.0 s

• Trials context: per_block

Preprocessing

• Data state: raw

• Preprocessing applied: False

Signal Processing

• Classifiers: Gaussian classifier

• Feature extraction: event-related potentials

• Frequency bands: analyzed=[1.0, 10.0] Hz

Cross-Validation

• Method: train-test split

• Evaluation type: cross_session

Performance (Original Study)

• Accuracy: 75.8%

• Correct Recognition Rate: 63.2

• Error Recognition Rate: 75.8

BCI Application

• Applications: error_detection

• Environment: laboratory

• Online feedback: False

Tags

• Pathology: Healthy

• Modality: Cognitive

• Type: ErrP

Documentation

• Description: Dataset on EEG error-related potentials (ErrPs) elicited when users monitor the behavior of an external autonomous agent. One of the first studies showing that error correlates can be observed and decoded during monitoring of external agents without user control.

• DOI: 10.1109/TNSRE.2010.2053387

• License: CC-BY-NC-ND-4.0

• Investigators: Ricardo Chavarriaga, José del R. Millán

• Senior author: José del R. Millán

• Contact: ricardo.chavarriaga@epfl.ch; jose.millan@epfl.ch

• Institution: Ecole Polytechnique Fédérale de Lausanne

• Department: Defitech Chair in Brain-Machine Interface, CNBI, Center for Neuroprosthetics

• Country: CH

• Repository: BNCI Horizon

• Publication year: 2010

• Funding: EC under Contract BACS FP6-IST-027140

• Keywords: error-related potentials, ErrP, brain-computer interface, reinforcement learning, monitoring, error detection

References

Chavarriaga, R., & Millán, J. D. R. (2010). Learning from EEG error-related potentials in noninvasive brain-computer interfaces. IEEE Trans. Neural Syst. Rehabil. Eng., 18(4), 381-388. https://doi.org/10.1109/TNSRE.2010.2053387 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	NM000168
Title	BNCI 2015-013 Error-Related Potentials dataset
Author (year)	Chavarriaga2015
Canonical	Chavarriaga2010
Importable as	NM000168, Chavarriaga2015, Chavarriaga2010
Year	2010
Authors	Ricardo Chavarriaga, José del R. Millán
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: 6

• Recordings: 120

• Tasks: 1

Channels & sampling rate

• Channels: 64

• Sampling rate (Hz): 512.0

• Duration (hours): 6.0910460069444445

Tags

• Pathology: Healthy

• Modality: Visual

• Type: Attention

Files & format

• Size on disk: 2.0 GB

• File count: 120

• Format: BIDS

License & citation

• License: CC-BY-NC-ND-4.0

• DOI: —

Provenance

• Source: nemar

• OpenNeuro: nm000168

• NeMAR: nm000168

API Reference

Use the NM000168 class to access this dataset programmatically.

class eegdash.dataset.NM000168(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

BNCI 2015-013 Error-Related Potentials dataset

Study:

nm000168 (NeMAR)

Author (year):

Chavarriaga2015

Canonical:

Chavarriaga2010

Also importable as: NM000168, Chavarriaga2015, Chavarriaga2010.

Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 6; recordings: 120; 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/nm000168 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000168

Examples

>>> from eegdash.dataset import NM000168
>>> dataset = NM000168(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

__init__(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

save(path, overwrite=False)

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

• eegdash.dataset.EEGDashDataset

• eegdash.dataset

• OpenNeuro dataset page

• NeMAR dataset page

• eegdash.dataset.DS001785

• eegdash.dataset.DS001787

• eegdash.dataset.DS001810

• eegdash.dataset.DS001849

• eegdash.dataset.DS001971