EEGdash›NeMAR›NM000193

Iss. 193 · 11 subjects · 66 recordings · CC0-1.0

Dataset Brief · Kojima et al. 2024 (Dataset A) — An auditory brain-computer i…

NM000193: eeg dataset, 11 subjects#

Kojima et al. 2024 (Dataset A) — An auditory brain-computer interface based on selective attention to multiple tone streams

Citation: Simon Kojima, Shin’ichiro Kanoh (2024). Kojima et al. 2024 (Dataset A) — An auditory brain-computer interface based on selective attention to multiple tone streams.

11-participant EEG dataset — Kojima et al. 2024 (Dataset A) — An auditory brain-computer interface based on selective attention to multiple tone streams.

Data & curation Simon Kojima · Shin'ichiro Kanoh
Year 2024 · Distributed via NeMAR
Funding JSPS KAKENHI Grant Number JP23K11811

EEG · 64 ch1000 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 NM000193

dataset = NM000193(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 = NM000193(cache_dir="./data", subject="01")

Advanced query

dataset = NM000193(
    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{nm000193,
  title = {Kojima et al. 2024 (Dataset A) — An auditory brain-computer interface based on selective attention to multiple tone streams},
  author = {Simon Kojima and Shin'ichiro Kanoh},
}

§ 02Study · The README

About This Dataset#

Class for Kojima2024A dataset management. P300 dataset.

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

Class for Kojima2024A dataset management. P300 dataset

Target

├─ Sensory-event
├─ Experimental-stimulus

View full README

Class for Kojima2024A dataset management. P300 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
Stimulus onset asynchrony: 180.0 ms

Data Structure

Blocks per session: 6
Block duration: 300.0 s
Trials context: Each task block had 3 runs (5 minutes each). Subjects counted target stimuli in Streams 1, 2, and 3 on the 1st, 2nd, and 3rd measurements respectively. Task block was repeated twice.

Preprocessing

Data state: raw
Preprocessing applied: False

Signal Processing

Classifiers: Logistic Regression, Minimum Distance to Mean (MDM)
Feature extraction: xDAWN spatial filtering, Riemannian geometry covariance matrices
Frequency bands: analyzed=[1.0, 40.0] Hz
Spatial filters: xDAWN

Cross-Validation

Method: 10-fold cross validation
Folds: 10
Evaluation type: within-subject

Performance (Original Study)

Description: Classification accuracy over 80% for 5 subjects, over 75% for 9 subjects
Metric: MCC (Matthews correlation coefficient)

BCI Application

Applications: communication
Environment: laboratory
Online feedback: False

Tags

Pathology: Healthy
Modality: auditory
Type: EEG, P300, BCI

Documentation

Description: A 3-class auditory BCI using three tone sequences based on auditory stream segregation. Musical tones were presented to subjects’ right ear, and subjects attended to one of three streams while counting target stimuli. P300 activity was elicited by target stimuli in the attended stream.
DOI: 10.1371/journal.pone.0303565
Associated paper DOI: 10.1371/journal.pone.0303565
License: CC0-1.0
Investigators: Simon Kojima, Shin’ichiro Kanoh
Senior author: Shin’ichiro Kanoh
Contact: nb21106@shibaura-it.ac.jp
Institution: Shibaura Institute of Technology
Department: Graduate School of Engineering and Science; College of Engineering
Address: Koto-ku, Tokyo, Japan
Country: JP
Repository: Harvard dataverse
Data URL: https://doi.org/10.7910/DVN/MQOVEY
Publication year: 2024
Funding: JSPS KAKENHI Grant Number JP23K11811
Ethics approval: Review Board on Bioengineering Research Ethics of Shibaura Institute of Technology; Declaration of Helsinki
Keywords: auditory BCI, P300, auditory stream segregation, selective attention, oddball paradigm, Riemannian geometry

External Links

Source: https://doi.org/10.7910/DVN/MQOVEY
Paper: https://doi.org/10.1371/journal.pone.0303565

Abstract

In this study, we attempted to improve brain-computer interface (BCI) systems by means of auditory stream segregation in which alternately presented tones are perceived as sequences of various different tones (streams). A 3-class BCI using three tone sequences, which were perceived as three different tone streams, was investigated and evaluated. Each presented musical tone was generated by a software synthesizer. Eleven subjects took part in the experiment. Stimuli were presented to each user’s right ear. Subjects were requested to attend to one of three streams and to count the number of target stimuli in the attended stream. In addition, 64-channel electroencephalogram (EEG) and two-channel electrooculogram (EOG) signals were recorded from participants with a sampling frequency of 1000 Hz. The measured EEG data were classified based on Riemannian geometry to detect the object of the subject’s selective attention. P300 activity was elicited by the target stimuli in the segregated tone streams. In five out of eleven subjects, P300 activity was elicited only by the target stimuli included in the attended stream. In a 10-fold cross validation test, a classification accuracy over 80% for five subjects and over 75% for nine subjects was achieved. For subjects whose accuracy was lower than 75%, either the P300 was also elicited for nonattended streams or the amplitude of P300 was small. It was concluded that the number of selected BCI systems based on auditory stream segregation can be increased to three classes, and these classes can be detected by a single ear without the aid of any visual modality.

Methodology

Musical tones generated by a digital auditory workstation were used as auditory stimuli. Piano tones from a MIDI sound source were presented using a digital signal processor and headphones to participants’ right ear only. Three tone streams were created using auditory stream segregation, each consisting of standard (90% probability) and deviant (10% probability) tones. The duration of each tone was 150 ms with stimulus onset asynchrony of 180 ms. The 64-channel EEG and 2-channel EOG signals were recorded at 1000 Hz. Each experiment consisted of two task blocks with three runs each (5 minutes per run). Subjects counted target stimuli in different streams across runs. Data analysis involved bandpass filtering (0.1-40 Hz for ERP analysis, 1-40 Hz for classification), baseline correction, artifact rejection (±100μV for EEG, ±500μV for EOG), xDAWN spatial filtering, and classification using Riemannian geometry with covariance matrices and logistic regression. Performance was evaluated using 10-fold cross validation with accuracy and Matthews correlation coefficient (MCC) metrics.

References

Kojima, S. (2024). Replication Data for: An auditory brain-computer interface based on selective attention to multiple tone streams. Harvard Dataverse, V1. DOI: https://doi.org/10.7910/DVN/MQOVEY Kojima, S. & Kanoh, S. (2024). An auditory brain-computer interface based on selective attention to multiple tone streams. PLoS ONE 19(5): e0303565. DOI: https://doi.org/10.1371/journal.pone.0303565 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 22–22 yr, mean 22.0 yr)

20

Female · 1Male · 10

Sex composition

Female
1
Male
10
F : M ratio
0.10 : 1

9% female · n = 11 subjects with reported sex.

Channel counts: 64 ch (n=66 recordings)

Sampling frequencies: 1000.0 Hz (n=66 recordings)

Total recording duration: 5 h 47 min

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 64 ch · EEG · 1000 Hz · 11 subjects, 66 recordings

Live trace viewer — sub-6 · ses-0 · task-p300 · run-4

Showing one representative recording out of 11 subjects and 66 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.NM000193(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)Kojima2024A_P300

Canonical—

Importable asNM000193 · Kojima2024A_P300

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

class eegdash.dataset.NM000193(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Kojima et al. 2024 (Dataset A) — An auditory brain-computer interface based on selective attention to multiple tone streams

Study:: nm000193 (NeMAR)
Author (year):: Kojima2024A_P300
Canonical:: —

Also importable as: NM000193, Kojima2024A_P300.

Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 11; recordings: 66; 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/nm000193 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000193

Examples

>>> from eegdash.dataset import NM000193
>>> dataset = NM000193(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 — NM000193.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 nm000193 to reproduce the tutorial on this dataset.

Citation

Simon Kojima, Shin'ichiro Kanoh (2024). Kojima et al. 2024 (Dataset A) — An auditory brain-computer interface based on selective attention to multiple tone streams.

Provenance

¹Contributed to nemar in BIDS format.

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

Related & sibling datasets

NM000207EEG · 15 subjSame authors NM000190EEG · 10 subj NM000192EEG · 11 subj NM000189EEG · 10 subj NM000234EEG · 21 subj

+ 1 more — see See Also below →

BIDS

BIDS 1.9.0

Sidecars

events · events.json · channels · eeg.json

Provenance

CC0-1.0 · DOI not on file

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HF org

Dataset ID	`NM000193`
Title	Kojima et al. 2024 (Dataset A) — An auditory brain-computer interface based on selective attention to multiple tone streams
Author (year)	`Kojima2024A_P300`
Canonical	—
Importable as	`NM000193`, `Kojima2024A_P300`
Year	2024
Authors	Simon Kojima, Shin’ichiro Kanoh
License	CC0-1.0
Citation / DOI	Unknown
Source links	OpenNeuro \| NeMAR \| Source URL

NM000193: 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#