NM000118: eeg dataset, 9 subjects#

Name: Nakanishi2015 – SSVEP Nakanishi 2015 dataset
Published: 2019

Nakanishi2015 – SSVEP Nakanishi 2015 dataset

Access recordings and metadata through EEGDash.

Citation: Masaki Nakanishi, Yijun Wang, Yu-Te Wang, Tzyy-Ping Jung (2019). Nakanishi2015 – SSVEP Nakanishi 2015 dataset.

Modality: eeg Subjects: 9 Recordings: 9 License: — Source: nemar

Metadata: Good (80%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import NM000118

dataset = NM000118(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 = NM000118(cache_dir="./data", subject="01")

Advanced query

dataset = NM000118(
    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{nm000118,
  title = {Nakanishi2015 – SSVEP Nakanishi 2015 dataset},
  author = {Masaki Nakanishi and Yijun Wang and Yu-Te Wang and Tzyy-Ping Jung},
}

About This Dataset#

SSVEP Nakanishi 2015 dataset

SSVEP Nakanishi 2015 dataset.

Dataset Overview

Code: Nakanishi2015
Paradigm: ssvep
DOI: 10.1371/journal.pone.0140703

Dataset Information#

Dataset ID	`NM000118`
Title	Nakanishi2015 – SSVEP Nakanishi 2015 dataset
Author (year)	`Nakanishi2015`
Canonical	—
Importable as	`NM000118`, `Nakanishi2015`
Year	2019
Authors	Masaki Nakanishi, Yijun Wang, Yu-Te Wang, Tzyy-Ping Jung
License	—
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: 9
Recordings: 9
Tasks: 1

Channels & sampling rate

Channels: 8
Sampling rate (Hz): 256.0
Duration (hours): 2.133974609375

API Reference#

Use the NM000118 class to access this dataset programmatically.

class eegdash.dataset.NM000118(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

Nakanishi2015 – SSVEP Nakanishi 2015 dataset

Study:: nm000118 (NeMAR)
Author (year):: Nakanishi2015
Canonical:: —

Also importable as: NM000118, Nakanishi2015.

Modality: eeg; Experiment type: Perception; Subject type: Healthy. Subjects: 9; recordings: 9; 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/nm000118 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000118

Examples

>>> from eegdash.dataset import NM000118
>>> dataset = NM000118(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