EEGdash›NeMAR›NM000168

Iss. 168 · 6 subjects · 120 recordings · CC-BY-NC-ND-4.0

Dataset Brief · BNCI 2015-013 Error-Related Potentials dataset

NM000168: eeg dataset, 6 subjects#

Name: BNCI 2015-013 Error-Related Potentials dataset
Published: 2010-01-01
License: CC-BY-NC-ND-4.0

BNCI 2015-013 Error-Related Potentials dataset

Citation: Ricardo Chavarriaga, José del R. Millán (2010). BNCI 2015-013 Error-Related Potentials dataset. 10.82901/nemar.nm000168

6-participant EEG dataset — BNCI 2015-013 Error-Related Potentials dataset.

Data & curation Ricardo Chavarriaga · José del R. Millán
Year 2010 · Distributed via NeMAR
Funding EC under Contract BACS FP6-IST-027140

EEG · 64 ch512 HzBIDS 1.9.0Task · p30020 sessionsHealthyVisualAttention

Layer 01Study

What was asked

Hypothesis, independent & dependent variables, paradigm, cohort, and the editorial caveats around what the recordings can and cannot answer.

Layer 02Signal · BIDS

What was recorded

Sidecars, channels & electrodes, coordinate system, event semantics, and quality stats from the NEMAR pipeline when available.

Layer 03Training · ML

What you can train on

Recommended access modes — MNE Raw, braindecode windows, PyTorch DataLoader — plus the targets the metadata makes addressable.

§ 01Access · Get started

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},
  doi = {10.82901/nemar.nm000168},
  url = {https://doi.org/10.82901/nemar.nm000168},
}

§ 02Study · The README

About This Dataset#

BNCI 2015-013 Error-Related Potentials dataset.

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

DOI

BNCI 2015-013 Error-Related Potentials dataset

Target

View full README

DOI

BNCI 2015-013 Error-Related Potentials dataset

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) NeuroTechX/moabb

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Age distribution by gender (n=6, range 28–28 yr, mean 27.0 yr)

25

Other · 6

Channel counts: 64 ch (n=120 recordings)

Sampling frequencies: 512.0 Hz (n=120 recordings)

Total recording duration: 6 h 5 min

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 64 ch · EEG · 512 Hz · 6 subjects, 120 recordings

Live trace viewer — sub-6 · ses-19 · task-p300 · run-0

Showing one representative recording out of 6 subjects and 120 recordings in this dataset. Browse the full set on OpenNeuro; drop any other _eeg.{set,edf,bdf,vhdr} file onto the viewer (or pass ?eeg=<url>) to inspect it.

Electrode layout — EEG · 64 sensors — 64 channels

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

§ 05Manifest · BIDS tree

Manifest#

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.

Recordings—

Files—

Subjects—

Modalities—

Click to load file structure…

§ 06API · Programmatic access

API Reference#

Signature

eegdash.dataset

class

eegdash.dataset.NM000168(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)Chavarriaga2015

Canonical—

Importable asNM000168 · Chavarriaga2015

Sourceeegdash/dataset/registry.py · [source ↗]

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

BNCI 2015-013 Error-Related Potentials dataset

Study:: nm000168 (NeMAR)
Author (year):: Chavarriaga2015
Canonical:: —

Also importable as: NM000168, Chavarriaga2015.

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 DOI: https://doi.org/10.82901/nemar.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)[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)

Parameters:

path (str) –

Directory in which subdirectories are created to store
-raw.fif | -epo.fif and .json files to.
overwrite (bool) – Whether to delete old subdirectories that will be saved to in this call.
offset (int) – If provided, the integer is added to the id of the dataset in the concat. This is useful in the setting of very large datasets, where one dataset has to be processed and saved at a time to account for its original position.

Access modesMNE → braindecode → PyTorch → ML

.rawMNE Raw object — standard tools (filter, epoch, ICA, plot_psd).mne

BaseConcatDatasetEach record is a lazy BaseDataset from braindecode — windowed via create_windows_from_events.braindecode

DataLoaderWraps the windowed dataset into a PyTorch DataLoader; supports parallel workers and on-the-fly augmentations.pytorch

Zarr cacheOptional braindecode Zarr mirror for fast resume; persisted to cache_dir.zarr

Hugging FaceNo per-dataset mirror published yet — browse the EEGDash org listing for sibling datasets. See the datasets loader API.huggingface

Croissant 1.0Machine-readable JSON-LD descriptor — NM000168.croissant.json (MLCommons schema, ingestible by PyTorch / TensorFlow / JAX).mlcommons

Examples using EEGDashcurated · start here

Find datasets with the EEGDash APIQuery the catalogue, filter by task or modality, list candidates.

Load one EEG recordingResolve a single record to an MNE Raw with channels and events.

EEG recording to PyTorch DataLoaderWrap braindecode windows in a DataLoader for model training.

Preprocess EEG and create windowsFilter, resample, epoch — and persist the windowed dataset.

Save and reload prepared dataCache a windowed dataset to disk and reattach it without recompute.

Download a dataset locallyPrefetch BIDS files to a local cache and validate the layout.

Swap any load_dataset(...) call for nm000168 to reproduce the tutorial on this dataset.

Citation

Ricardo Chavarriaga, José del R. Millán (2010). BNCI 2015-013 Error-Related Potentials dataset. 10.82901/nemar.nm000168

Provenance

¹Contributed to nemar in BIDS format.

²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.

³Persistent identifier: 10.82901/nemar.nm000168.

Related & sibling datasets

NM000206EEG · 15 subj NM000343EEG · 15 subj NM000346EEG · 12 subj NM000163EEG · 12 subj NM000344EEG · 12 subj

+ 1 more — see See Also below →

BIDS

BIDS 1.9.0

Sidecars

events · events.json · channels · eeg.json

Provenance

CC-BY-NC-ND-4.0 · 10.82901/nemar.nm000168

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HF org

Dataset ID	`NM000168`
Title	BNCI 2015-013 Error-Related Potentials dataset
Author (year)	`Chavarriaga2015`
Canonical	—
Importable as	`NM000168`, `Chavarriaga2015`
Year	2010
Authors	Ricardo Chavarriaga, José del R. Millán
License	CC-BY-NC-ND-4.0
Citation / DOI	10.82901/nemar.nm000168
Source links	OpenNeuro \| NeMAR \| Source URL

NM000168: eeg dataset, 6 subjects#

Quickstart#

About This Dataset#

Cohort#

Dataset Statistics#

Signal · Electrodes & live trace#

NEMAR Processing Statistics#

Manifest#

File Explorer#

API Reference#

Citation

Provenance

Related & sibling datasets

See Also#