NM000207: eeg dataset, 15 subjects#

Class for Kojima2024B dataset management. P300 dataset

Access recordings and metadata through EEGDash.

Citation: Simon Kojima, Shin’ichiro Kanoh (2024). Class for Kojima2024B dataset management. P300 dataset.

Modality: eeg Subjects: 15 Recordings: 180 License: CC0-1.0 Source: nemar

Metadata: Complete (90%)

Quickstart#

Install

pip install eegdash

Access the data

from eegdash.dataset import NM000207

dataset = NM000207(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Filter by subject

dataset = NM000207(cache_dir="./data", subject="01")

Advanced query

dataset = NM000207(
    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{nm000207,
  title = {Class for Kojima2024B dataset management. P300 dataset},
  author = {Simon Kojima and Shin'ichiro Kanoh},
}

About This Dataset#

Class for Kojima2024B dataset management. P300 dataset

Class for Kojima2024B dataset management. P300 dataset.

Dataset Overview

  • Code: Kojima2024B

  • Paradigm: p300

  • DOI: 10.7910/DVN/1UJDV6

View full README

Class for Kojima2024B dataset management. P300 dataset

Class for Kojima2024B dataset management. P300 dataset.

Dataset Overview

  • Code: Kojima2024B

  • Paradigm: p300

  • DOI: 10.7910/DVN/1UJDV6

  • Subjects: 15

  • Sessions per subject: 1

  • Events: Target=[111, 112, 113, 114], NonTarget=[101, 102, 103, 104]

  • Trial interval: [-0.5, 1.2] s

  • Runs per session: 12

  • File format: BrainVision

  • Number of contributing labs: 1

Acquisition

  • Sampling rate: 1000.0 Hz

  • Number of channels: 64

  • Channel types: eeg=64, eog=2

  • Channel names: AF3, AF4, AF7, AF8, AFz, C1, C2, C3, C4, C5, C6, CP1, CP2, CP3, CP4, CP5, CP6, CPz, Cz, F1, F2, F3, F4, F5, F6, F7, F8, FC1, FC2, FC3, FC4, FC5, FC6, FCz, FT10, FT7, FT8, FT9, Fp1, Fp2, Fz, O1, O2, Oz, P1, P2, P3, P4, P5, P6, P7, P8, PO3, PO4, PO7, PO8, POz, Pz, T7, T8, TP10, TP7, TP8, TP9, hEOG, vEOG

  • Montage: standard_1020

  • Hardware: BrainAmp

  • Reference: right mastoid

  • Ground: left mastoid

  • Sensor type: EEG

  • Line frequency: 50.0 Hz

  • Cap manufacturer: EasyCap

  • Electrode type: passive Ag/AgCl

  • Electrode material: Ag/AgCl

  • Auxiliary channels: EOG (2 ch, vertical, horizontal)

Participants

  • Number of subjects: 15

  • Health status: healthy

  • Age: mean=22.8, min=21.0, max=24.0

  • Gender distribution: male=13, female=2

  • Species: human

Experimental Protocol

  • Paradigm: p300

  • Task type: auditory stream segregation with oddball

  • Number of classes: 2

  • Class labels: Target, NonTarget

  • Trial duration: 90.0 s

  • Tasks: ASME-4stream, ASME-2stream

  • Study design: within-subject comparison

  • Study domain: auditory BCI

  • Feedback type: none

  • Stimulus type: auditory tones

  • Stimulus modalities: auditory

  • Primary modality: auditory

  • Synchronicity: synchronous

  • Mode: offline

  • Training/test split: False

  • Instructions: focus selectively on deviant stimuli in one of the streams and count target deviant stimuli

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: 4

  • Number of repetitions: 15

  • Stimulus onset asynchrony: {‘ASME-4stream_overall’: 150.0, ‘ASME-2stream_overall’: 300.0, ‘within_stream’: 600.0} ms

Data Structure

  • Trials: {‘ASME-4stream’: ‘600 stimuli per trial (4 trials per run, 6 runs)’, ‘ASME-2stream’: ‘300 stimuli per trial (4 trials per run, 6 runs)’}

  • Blocks per session: 12

  • Block duration: 90.0 s

  • Trials context: 12 runs alternating between ASME-4stream and ASME-2stream, 4 trials per run

Preprocessing

  • Data state: raw

  • Preprocessing applied: False

Signal Processing

  • Classifiers: Linear Discriminant Analysis (LDA), shrinkage-LDA

  • Feature extraction: mean amplitudes in 10 intervals (0.1s non-overlapping, 0-1.0s)

  • Frequency bands: analyzed=[0.1, 8.0] Hz

Cross-Validation

  • Method: 3-fold chronological cross-validation (BCI simulation); 4-fold chronological cross-validation (binary classification)

  • Evaluation type: offline simulation

Performance (Original Study)

  • Asme-4Stream Accuracy: 0.83

  • Asme-2Stream Accuracy: 0.86

BCI Application

  • Applications: communication

  • Environment: laboratory

  • Online feedback: False

Tags

  • Pathology: Healthy

  • Modality: auditory

  • Type: ERP, P300

Documentation

  • Description: Four-class ASME BCI investigation comparing two strategies for multiclassing: ASME-4stream (four streams with single target stimulus each) vs ASME-2stream (two streams with two target stimuli each)

  • DOI: 10.3389/fnhum.2024.1461960

  • Associated paper DOI: 10.3389/fnhum.2024.1461960

  • License: CC0-1.0

  • Investigators: Simon Kojima, Shin’ichiro Kanoh

  • Senior author: Shin’ichiro Kanoh

  • Contact: simon.kojima@ieee.org

  • Institution: Shibaura Institute of Technology

  • Department: Graduate School of Engineering and Science (Simon Kojima); College of Engineering (Shin’ichiro Kanoh)

  • Address: Tokyo, Japan

  • Country: JP

  • Repository: Harvard dataverse

  • Data URL: https://doi.org/10.7910/DVN/1UJDV6

  • Publication year: 2024

  • Funding: JSPS KAKENHI (Grant Number JP23K11811 to Shin’ichiro Kanoh)

  • Ethics approval: Review Board on Bioengineering Research Ethics of the Shibaura Institute of Technology

  • Keywords: brain-computer interface, electroencephalogram, event-related potential, auditory scene analysis, stream segregation, machine learning, NASA-TLX

Abstract

The ASME (Auditory Stream segregation Multiclass ERP) paradigm is used for an auditory brain-computer interface (BCI). Two approaches for achieving four-class ASME were investigated: ASME-4stream (four streams with a single target stimulus each) and ASME-2stream (two streams with two target stimuli each). Fifteen healthy subjects participated. ERPs were analyzed, and binary classification and BCI simulation were conducted offline using linear discriminant analysis. Average accuracies were 0.83 (ASME-4stream) and 0.86 (ASME-2stream). The ASME-2stream paradigm showed shorter latency and larger amplitude of P300, higher binary classification accuracy, and smaller workload. Both paradigms achieved sufficiently high accuracy (over 80%) for practical auditory BCI.

Methodology

Subjects performed 12 runs alternating between ASME-4stream and ASME-2stream paradigms. Each run contained 4 trials with ~90s duration. ASME-4stream presented 4 streams (SOA=0.15s, 600 stimuli/trial, ratio 9:1 standard:deviant). ASME-2stream presented 2 streams with 2 deviant stimuli each (SOA=0.3s, 300 stimuli/trial, ratio 8:1:1). EEG recorded at 1000 Hz from 64 channels. EOG artifacts removed using ICA on 15 PCs. Data filtered (1-40 Hz for ERP, 0.1-8 Hz for classification), epoched (-0.1 to 1.2s), downsampled to 250 Hz. Classification used shrinkage-LDA with mean amplitudes from 10 intervals (0-1.0s) as features. Performance evaluated using 4-fold chronological cross-validation. Usability assessed via NASA-TLX questionnaire.

References

Kojima, S. (2024). Replication Data for: Four-class ASME BCI: investigation of the feasibility and comparison of two strategies for multiclassing. Harvard Dataverse, V1. DOI: https://doi.org/10.7910/DVN/1UJDV6 Kojima, S. & Kanoh, S. (2024). Four-class ASME BCI: investigation of the feasibility and comparison of two strategies for multiclassing. Frontiers in Human Neuroscience 18:1461960. DOI: https://doi.org/10.3389/fnhum.2024.1461960 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

NM000207

Title

Class for Kojima2024B dataset management. P300 dataset

Author (year)

Kojima2024B_P300

Canonical

—

Importable as

NM000207, Kojima2024B_P300

Year

2024

Authors

Simon Kojima, Shin’ichiro Kanoh

License

CC0-1.0

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: 15

  • Recordings: 180

  • Tasks: 1

Channels & sampling rate

  • Channels: 64

  • Sampling rate (Hz): 1000.0

  • Duration (hours): 21.62847222222222

Tags

  • Pathology: Healthy

  • Modality: Auditory

  • Type: Attention

Files & format

  • Size on disk: 13.9 GB

  • File count: 180

  • Format: BIDS

License & citation

  • License: CC0-1.0

  • DOI: —

Provenance

API Reference#

Use the NM000207 class to access this dataset programmatically.

class eegdash.dataset.NM000207(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

Class for Kojima2024B dataset management. P300 dataset

Study:

nm000207 (NeMAR)

Author (year):

Kojima2024B_P300

Canonical:

—

Also importable as: NM000207, Kojima2024B_P300.

Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 15; recordings: 180; 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/nm000207 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000207

Examples

>>> from eegdash.dataset import NM000207
>>> dataset = NM000207(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#