NM000162: eeg dataset, 20 subjects#

Name: BNCI 2025-001 Motor Kinematics Reaching dataset
Published: 2024
License: CC-BY-4.0

BNCI 2025-001 Motor Kinematics Reaching dataset

Access recordings and metadata through EEGDash.

Citation: Nitikorn Srisrisawang, Gernot R Müller-Putz (2024). BNCI 2025-001 Motor Kinematics Reaching dataset.

Modality: eeg Subjects: 20 Recordings: 20 License: CC-BY-4.0 Source: nemar

Metadata: Complete (90%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import NM000162

dataset = NM000162(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 = NM000162(cache_dir="./data", subject="01")

Advanced query

dataset = NM000162(
    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{nm000162,
  title = {BNCI 2025-001 Motor Kinematics Reaching dataset},
  author = {Nitikorn Srisrisawang and Gernot R Müller-Putz},
}

About This Dataset#

BNCI 2025-001 Motor Kinematics Reaching dataset

BNCI 2025-001 Motor Kinematics Reaching dataset.

Dataset Overview

Code: BNCI2025-001
Paradigm: imagery
DOI: 10.1088/1741-2552/ada0ea

Dataset Information#

Dataset ID	`NM000162`
Title	BNCI 2025-001 Motor Kinematics Reaching dataset
Author (year)	`Srisrisawang2025`
Canonical	`BNCI2025`
Importable as	`NM000162`, `Srisrisawang2025`, `BNCI2025`
Year	2024
Authors	Nitikorn Srisrisawang, Gernot R Müller-Putz
License	CC-BY-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: 20
Recordings: 20
Tasks: 1

Channels & sampling rate

Channels: 67
Sampling rate (Hz): 500.0
Duration (hours): 44.44853333333333

API Reference#

Use the NM000162 class to access this dataset programmatically.

class eegdash.dataset.NM000162(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

BNCI 2025-001 Motor Kinematics Reaching dataset

Study:: nm000162 (NeMAR)
Author (year):: Srisrisawang2025
Canonical:: BNCI2025

Also importable as: NM000162, Srisrisawang2025, BNCI2025.

Modality: eeg; Experiment type: Motor; Subject type: Healthy. Subjects: 20; recordings: 20; 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/nm000162 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000162

Examples

>>> from eegdash.dataset import NM000162
>>> dataset = NM000162(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