EEGdash›NeMAR›NM000263

Iss. 263 · 10 subjects · 10 recordings · CC-BY-3.0

Dataset Brief · Visual object ERP EEG dataset (Kaneshiro et al. 2015)

NM000263: eeg dataset, 10 subjects#

Name: Visual object ERP EEG dataset (Kaneshiro et al. 2015)
License: CC-BY-3.0

Visual object ERP EEG dataset (Kaneshiro et al. 2015)

Access recordings and metadata through EEGDash.

Citation: Blair Kaneshiro, Marcos Perreau Guimaraes, Hyung-Suk Kim, Anthony M. Norcia, Patrick Suppes (20). Visual object ERP EEG dataset (Kaneshiro et al. 2015). 10.82901/nemar.nm000263

Modality: eeg Subjects: 10 Recordings: 10 License: CC-BY-3.0 Source: nemar

Metadata: Complete (100%)

10-participant EEG dataset — Visual object ERP EEG dataset (Kaneshiro et al. 2015).

Data & curation Blair Kaneshiro · Marcos Perreau Guimaraes · Hyung-Suk Kim · Anthony M. Norcia · Patrick Suppes
Senior author Patrick Suppes
Year 20 · Distributed via NeMAR

EEG · 124 ch62 HzBIDS 1.9.0Task · p300

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 NM000263

dataset = NM000263(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 = NM000263(cache_dir="./data", subject="01")

Advanced query

dataset = NM000263(
    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{nm000263,
  title = {Visual object ERP EEG dataset (Kaneshiro et al. 2015)},
  author = {Blair Kaneshiro and Marcos Perreau Guimaraes and Hyung-Suk Kim and Anthony M. Norcia and Patrick Suppes},
  doi = {10.82901/nemar.nm000263},
  url = {https://doi.org/10.82901/nemar.nm000263},
}

§ 02Study · The README

About This Dataset#

A visual event-related potential (ERP) dataset comprising 124-channel EEG recordings from 10 healthy participants viewing photographs from six object categories (human body, human face, animal body, animal face, fruit/vegetable, and inanimate objects). The dataset contains 5,184 trials collected during a visual object recognition task with 72 images per category, each presented for 500 ms with a 750 ms interstimulus interval. Data were acquired at 62.5 Hz using a high-density geodesic sensor net and are suitable for investigating neural representations of object categories through single-trial EEG classification and representational similarity analysis.

EEG data were acquired from 10 healthy participants using a 124-channel EGI Net Amps 300 system with HydroCel Geodesic Sensor Net montage (GSN-HydroCel-128) at a sampling rate of 62.5 Hz with average reference. Participants viewed 72 photographs from six object categories presented for 500 ms each with a 750 ms interstimulus interval. Data were preprocessed and organized in BIDS format.

DOI

Visual object ERP EEG dataset (Kaneshiro et al. 2015)

Overview

How to Access via MOABB

Install MOABB and load this dataset directly:

NEMAR Metadata#

[![DOI](https://img.shields.io/badge/DOI-10.82901%2Fnemar.nm000263-blue)](https://doi.org/10.82901/nemar.nm000263) # Visual object ERP EEG dataset (Kaneshiro et al. 2015) ## Overview A visual event-related potential (ERP) dataset comprising 124-channel EEG recordings from 10 healthy participants viewing photographs from six object categories (human body, human face, animal body, animal face, fruit/vegetable, and inanimate objects). The dataset contains 5,184 trials collected during a visual object recognition task with 72 images per category, each presented for 500 ms with a 750 ms interstimulus interval. Data were acquired at 62.5 Hz using a high-density geodesic sensor net and are suitable for investigating neural representations of object categories through single-trial EEG classification and representational similarity analysis. ## Dataset Summary | Property | Value | |---|—| | Subjects | 10 | | Channels | 124 | | Classes | 6 | | Trial length | 0.5 s | | Sampling frequency | 62.5 Hz | | Sessions | 1 | | Total trials | 5184 | | Paradigm | P300 | ## Data Collection Methods EEG data were acquired from 10 healthy participants using a 124-channel EGI Net Amps 300 system with HydroCel Geodesic Sensor Net montage (GSN-HydroCel-128) at a sampling rate of 62.5 Hz with average reference. Participants viewed 72 photographs from six object categories presented for 500 ms each with a 750 ms interstimulus interval. Data were preprocessed and organized in BIDS format. ## How to Access via MOABB Install MOABB and load this dataset directly: `python from moabb.datasets import Kaneshiro2015 from moabb.paradigms import P300 paradigm = P300() dataset = Kaneshiro2015() X, y, metadata = paradigm.get_data(dataset) ` For more details see the [MOABB documentation](https://moabb.neurotechx.com/) and the [MOABB dataset page](https://moabb.neurotechx.com/docs/generated/moabb.datasets.Kaneshiro2015.html). ## Citation If you use this dataset please cite the primary publication: > DOI: [10.1371/journal.pone.0135697](https://doi.org/10.1371/journal.pone.0135697) ## NEMAR / MOABB Benchmark Collection This BIDS-formatted dataset was converted from the original data using the [MOABB](https://moabb.neurotechx.com/) pipeline and re-hosted on [NEMAR](https://nemar.org/) as part of the MOABB benchmark collection. The original data and license terms apply — see dataset_description.json for details.

License: CC-BY-3.0

Authors:

Blair Kaneshiro
Marcos Perreau Guimaraes
Hyung-Suk Kim
Anthony M. Norcia
Patrick Suppes

Versions:

Version	DOI	Released
`current`	10.82901/nemar.nm000263

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Channel counts: 124 ch (n=10 recordings)

Sampling frequencies: 62.5 Hz (n=10 recordings)

Total recording duration: 8 h 45 min

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 124 ch · EEG · 62 Hz · 10 subjects, 10 recordings

Live trace viewer — sub-1 · ses-0 · task-p300 · run-0

Showing one representative recording out of 10 subjects and 10 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 · 124 sensors — 124 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.NM000263(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)—

Canonical—

Importable asNM000263

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

class eegdash.dataset.NM000263(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Visual object ERP EEG dataset (Kaneshiro et al. 2015)

Study:: nm000263 (NeMAR)
Author (year):: —
Canonical:: —

Also importable as: NM000263.

Modality: eeg; Subject type: Unknown. Subjects: 10; recordings: 10; 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/nm000263 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000263 DOI: https://doi.org/10.82901/nemar.nm000263

Examples

>>> from eegdash.dataset import NM000263
>>> dataset = NM000263(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 — NM000263.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 nm000263 to reproduce the tutorial on this dataset.

Citation

Blair Kaneshiro, Marcos Perreau Guimaraes, Hyung-Suk Kim, Anthony M. Norcia, Patrick Suppes (20). Visual object ERP EEG dataset (Kaneshiro et al. 2015). 10.82901/nemar.nm000263

Provenance

¹Contributed to nemar in BIDS format.

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

³Persistent identifier: 10.82901/nemar.nm000263.

Related & sibling datasets

ON007406EEG · 10 subj ON003626EEG · 10 subj ON006563EEG · 12 subj ON006647EEG · 4 subj ON005028EEG · 11 subj

+ 1 more — see See Also below →

BIDS

BIDS 1.9.0

Sidecars

events · events.json · channels · eeg.json

Provenance

CC-BY-3.0 · 10.82901/nemar.nm000263

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HF org

Dataset ID	`NM000263`
Title	Visual object ERP EEG dataset (Kaneshiro et al. 2015)
Author (year)	—
Canonical	—
Importable as	`NM000263`
Year	20
Authors	Blair Kaneshiro, Marcos Perreau Guimaraes, Hyung-Suk Kim, Anthony M. Norcia, Patrick Suppes
License	CC-BY-3.0
Citation / DOI	10.82901/nemar.nm000263
Source links	OpenNeuro \| NeMAR \| Source URL

NM000263: eeg dataset, 10 subjects#

Quickstart#

About This Dataset#

NEMAR Metadata#

Cohort#

Dataset Statistics#

Signal · Electrodes & live trace#

NEMAR Processing Statistics#

Manifest#

File Explorer#

API Reference#

Citation

Provenance

Related & sibling datasets

See Also#