EEGdash›NeMAR›NM000192

Iss. 192 · 11 subjects · 11 recordings · CC-BY-NC-ND-4.0

Dataset Brief · BNCI 2015-006 Music BCI dataset

NM000192: eeg dataset, 11 subjects#

Name: BNCI 2015-006 Music BCI dataset
Published: 2014-01-01
License: CC-BY-NC-ND-4.0

BNCI 2015-006 Music BCI dataset

Citation: M S Treder, H Purwins, D Miklody, I Sturm, B Blankertz (2014). BNCI 2015-006 Music BCI dataset.

11-participant EEG dataset — BNCI 2015-006 Music BCI dataset.

Data & curation M S Treder · H Purwins · D Miklody · I Sturm · B Blankertz
Year 2014 · Distributed via NeMAR
Funding German Bundesministerium für Bildung und Forschung (Grant Nos. 16SV5839 and 01GQ0850)

EEG · 64 ch200 HzBIDS 1.9.0Task · p300HealthyAuditoryAttention

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 NM000192

dataset = NM000192(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 = NM000192(cache_dir="./data", subject="01")

Advanced query

dataset = NM000192(
    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{nm000192,
  title = {BNCI 2015-006 Music BCI dataset},
  author = {M S Treder and H Purwins and D Miklody and I Sturm and B Blankertz},
}

§ 02Study · The README

About This Dataset#

BNCI 2015-006 Music BCI dataset.

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

BNCI 2015-006 Music BCI dataset

Target

├─ Sensory-event
├─ Experimental-stimulus

View full README

BNCI 2015-006 Music BCI dataset

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: 3

Data Structure

Trials: 3-7 deviants per instrument per clip
Blocks per session: 10
Trials context: per_instrument_per_clip

Preprocessing

Data state: epoched
Preprocessing applied: True
Steps: downsampling, lowpass filtering, epoching, baseline correction, artifact rejection
Lowpass filter: 42.0 Hz
Filter type: Chebyshev
Artifact methods: min-max criterion (100 μV threshold on Fp1 or Fp2)
Downsampled to: 250.0 Hz
Epoch window: [-0.2, 1.2]
Notes: Artifact rejection applied only to training set, preserved in test set. Passbands: 42 Hz, stopbands: 49 Hz for Chebyshev filter.

Signal Processing

Classifiers: LDA with shrinkage covariance
Feature extraction: spatio-temporal features, voltage averaging in time windows
Frequency bands: alpha=[8, 13] Hz

Cross-Validation

Method: leave-one-clip-out
Evaluation type: cross_trial

Performance (Original Study)

Accuracy: 91.0%
Binary Classifier Accuracy Synth Pop: 69.25
Binary Classifier Accuracy Jazz: 71.47
Posterior Probability Accuracy Synth Pop: 91.0
Posterior Probability Accuracy Jazz: 91.5

BCI Application

Applications: communication, speller, message selection
Environment: laboratory
Online feedback: False

Tags

Pathology: Healthy
Modality: Auditory
Type: Perception, Attention

Documentation

Description: Multi-streamed musical oddball paradigm for auditory BCI. Each of three concurrent instruments has its own standard and deviant patterns. Participants selectively attend to one instrument to detect deviants.
DOI: 10.1088/1741-2560/11/2/026009
Associated paper DOI: 10.1088/1741-2560/11/2/026009
License: CC-BY-NC-ND-4.0
Investigators: M S Treder, H Purwins, D Miklody, I Sturm, B Blankertz
Senior author: B Blankertz
Contact: matthias.treder@tu-berlin.de
Institution: Technische Universität Berlin
Department: Neurotechnology Group; Bernstein Focus: Neurotechnology
Address: Berlin, Germany
Country: Germany
Repository: GitHub
Data URL: bbci/bbci_public
Publication year: 2014
Funding: German Bundesministerium für Bildung und Forschung (Grant Nos. 16SV5839 and 01GQ0850)
Ethics approval: Declaration of Helsinki
Acknowledgements: We acknowledge financial support by the German Bundesministerium für Bildung und Forschung (Grant Nos. 16SV5839 and 01GQ0850).
Keywords: brain–computer interface, EEG, auditory, music, attention, oddball paradigm, P300

Abstract

Polyphonic music (music consisting of several instruments playing in parallel) is an intuitive way of embedding multiple information streams. The different instruments in a musical piece form concurrent information streams that seamlessly integrate into a coherent and hedonistically appealing entity. Here, we explore polyphonic music as a novel stimulation approach for use in a brain–computer interface. In a multi-streamed oddball experiment, we had participants shift selective attention to one out of three different instruments in music audio clips. Each instrument formed an oddball stream with its own specific standard stimuli (a repetitive musical pattern) and oddballs (deviating musical pattern). Contrasting attended versus unattended instruments, ERP analysis shows subject- and instrument-specific responses including P300 and early auditory components. The attended instrument can be classified offline with a mean accuracy of 91% across 11 participants. This is a proof of concept that attention paid to a particular instrument in polyphonic music can be inferred from ongoing EEG, a finding that is potentially relevant for both brain–computer interface and music research.

Methodology

Participants listened to 40-second polyphonic music clips with three concurrent instruments (Synth-Pop: bass, drums, keyboard; Jazz: double-bass, piano, flute). Each instrument had standard patterns and infrequent deviants (3-7 per clip). Participants were cued to attend to one instrument and count deviants. EEG recorded at 1000 Hz with 64 electrodes, downsampled to 250 Hz, lowpass filtered (Chebyshev, 42 Hz passband), epoched (-200 to 1200 ms), baseline corrected, and artifact rejected. Two classification approaches: (1) general binary classifier and (2) instrument-specific classifiers with posterior probabilities. Features: spatio-temporal (3 time intervals × 63 electrodes = 189 dimensions). LDA with shrinkage covariance. Leave-one-clip-out cross-validation. Main experiment: 10 blocks of 21 clips (7 clips per instrument as target). Total: 3 Synth-Pop mixed blocks, 3 Jazz mixed blocks, 2 Synth-Pop solo blocks, 2 Jazz solo blocks.

References

Treder, M. S., Purwins, H., Miklody, D., Sturm, I., & Blankertz, B. (2014). Decoding auditory attention to instruments in polyphonic music using single-trial EEG classification. Journal of Neural Engineering, 11(2), 026009. https://doi.org/10.1088/1741-2560/11/2/026009 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=11, range 28–28 yr, mean 28.0 yr)

25

Other · 11

Channel counts: 64 ch (n=11 recordings)

Sampling frequencies: 200.0 Hz (n=11 recordings)

Total recording duration: 33 h

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 64 ch · EEG · 200 Hz · 11 subjects, 11 recordings

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

Showing one representative recording out of 11 subjects and 11 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 · 63 sensors — 63 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.NM000192(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)Treder2015_BNCI_006_Music

Canonical—

Importable asNM000192 · Treder2015_BNCI_006_Music

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

class eegdash.dataset.NM000192(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

BNCI 2015-006 Music BCI dataset

Study:: nm000192 (NeMAR)
Author (year):: Treder2015_BNCI_006_Music
Canonical:: —

Also importable as: NM000192, Treder2015_BNCI_006_Music.

Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 11; recordings: 11; 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/nm000192 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000192

Examples

>>> from eegdash.dataset import NM000192
>>> dataset = NM000192(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 — NM000192.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 nm000192 to reproduce the tutorial on this dataset.

Citation

M S Treder, H Purwins, D Miklody, I Sturm, B Blankertz (2014). BNCI 2015-006 Music BCI dataset.

Provenance

¹Contributed to nemar in BIDS format.

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

Related & sibling datasets

NM000190EEG · 10 subj NM000189EEG · 10 subj NM000234EEG · 21 subj NM000193EEG · 11 subj NM000207EEG · 15 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 · DOI not on file

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HF org

Dataset ID	`NM000192`
Title	BNCI 2015-006 Music BCI dataset
Author (year)	`Treder2015_BNCI_006_Music`
Canonical	—
Importable as	`NM000192`, `Treder2015_BNCI_006_Music`
Year	2014
Authors	M S Treder, H Purwins, D Miklody, I Sturm, B Blankertz
License	CC-BY-NC-ND-4.0
Citation / DOI	Unknown
Source links	OpenNeuro \| NeMAR \| Source URL

NM000192: eeg dataset, 11 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#