NM000192: eeg dataset, 11 subjects#

BNCI 2015-006 Music BCI dataset

Access recordings and metadata through EEGDash.

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

Modality: eeg Subjects: 11 Recordings: 11 License: CC-BY-NC-ND-4.0 Source: nemar

Metadata: Complete (90%)

Quickstart#

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)

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'])

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},
}

About This Dataset#

BNCI 2015-006 Music BCI dataset

BNCI 2015-006 Music BCI dataset.

Dataset Overview

  • Code: BNCI2015-006

  • Paradigm: p300

  • DOI: 10.1088/1741-2560/11/2/026009

View full README

BNCI 2015-006 Music BCI dataset

BNCI 2015-006 Music BCI dataset.

Dataset Overview

  • Code: BNCI2015-006

  • Paradigm: p300

  • DOI: 10.1088/1741-2560/11/2/026009

  • Subjects: 11

  • Sessions per subject: 1

  • Events: Target=1, NonTarget=2

  • Trial interval: [0, 1.0] s

  • File format: gdf

  • Data preprocessed: True

  • Contributing labs: Neurotechnology Group TU Berlin, Bernstein Focus Neurotechnology, Aalborg University Copenhagen, Berlin School of Mind and Brain

Acquisition

  • Sampling rate: 200.0 Hz

  • Number of channels: 64

  • Channel types: eeg=64

  • Channel names: AF3, AF4, AF7, AF8, C1, C2, C3, C4, C5, C6, CP1, CP2, CP3, CP4, CP5, CP6, CPz, Cz, EOGvu, F1, F10, F2, F3, F4, F5, F6, F7, F8, F9, FC1, FC2, FC3, FC4, FC5, FC6, FCz, FT7, FT8, Fp1, Fp2, Fz, O1, O2, Oz, P1, P10, P2, P3, P4, P5, P6, P7, P8, P9, PO3, PO4, PO7, PO8, POz, Pz, T7, T8, TP7, TP8

  • Montage: 10-10

  • Hardware: Brain Products

  • Reference: left mastoid

  • Ground: forehead

  • Sensor type: active electrode

  • Line frequency: 50.0 Hz

  • Online filters: {‘bandpass’: [0.016, 250]}

  • Impedance threshold: 20.0 kOhm

  • Cap manufacturer: Brain Products

  • Cap model: actiCAP

  • Electrode type: active

Participants

  • Number of subjects: 11

  • Health status: patients

  • Clinical population: Healthy

  • Age: mean=28.0, min=21, max=50

  • Gender distribution: male=7, female=4

  • Handedness: all but one right-handed

  • BCI experience: naive

  • Species: human

Experimental Protocol

  • Paradigm: p300

  • Task type: auditory oddball

  • Number of classes: 2

  • Class labels: Target, NonTarget

  • Trial duration: 40.0 s

  • Tasks: selective auditory attention, deviant counting

  • Study design: Multi-streamed musical oddball paradigm with three concurrent instruments. Participants attended to one instrument and counted deviants while ignoring the other two instruments. Two music conditions tested: Synth-Pop (bass, drums, keyboard) and Jazz (double-bass, piano, flute).

  • Study domain: auditory BCI

  • Feedback type: none

  • Stimulus type: musical oddball

  • Stimulus modalities: visual, auditory

  • Primary modality: auditory

  • Synchronicity: asynchronous

  • Mode: offline

  • Training/test split: False

  • Instructions: Attend to cued instrument, count the number of deviants in that instrument, ignore other two instruments, maintain fixation on cross, minimize eye movements

  • Stimulus presentation: visual_cue=instrument indication, fixation_cross=continuous during music playback, music_clips=40-second polyphonic music

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

  • 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: https://github.com/bbci/bbci_public/blob/master/doc/index.markdown

  • 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) https://github.com/NeuroTechX/moabb

Dataset Information#

Dataset ID

NM000192

Title

BNCI 2015-006 Music BCI dataset

Author (year)

Treder2015_BNCI_006_Music

Canonical

BNCI2015_BNCI_006_Music, BNCI_2015_006_Music, BNCI2015_006_MusicBCI

Importable as

NM000192, Treder2015_BNCI_006_Music, BNCI2015_BNCI_006_Music, BNCI_2015_006_Music, BNCI2015_006_MusicBCI

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

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: 11

  • Recordings: 11

  • Tasks: 1

Channels & sampling rate

  • Channels: 64

  • Sampling rate (Hz): 200.0

  • Duration (hours): 33.94770694444444

Tags

  • Pathology: Healthy

  • Modality: Auditory

  • Type: Attention

Files & format

  • Size on disk: 4.4 GB

  • File count: 11

  • Format: BIDS

License & citation

  • License: CC-BY-NC-ND-4.0

  • DOI: —

Provenance

API Reference#

Use the NM000192 class to access this dataset programmatically.

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

Bases: EEGDashDataset

BNCI 2015-006 Music BCI dataset

Study:

nm000192 (NeMAR)

Author (year):

Treder2015_BNCI_006_Music

Canonical:

BNCI2015_BNCI_006_Music, BNCI_2015_006_Music, BNCI2015_006_MusicBCI

Also importable as: NM000192, Treder2015_BNCI_006_Music, BNCI2015_BNCI_006_Music, BNCI_2015_006_Music, BNCI2015_006_MusicBCI.

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, 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#