NM000329: eeg dataset, 16 subjects#

Name: Brandl2020
Published: 2020
License: CC-BY-NC-ND-4.0

Brandl2020

Access recordings and metadata through EEGDash.

Citation: Stephanie Brandl, Benjamin Blankertz, Tobias Dahne (2020). Brandl2020. 10.3389/fnins.2020.566147

Modality: eeg Subjects: 16 Recordings: 112 License: CC-BY-NC-ND-4.0 Source: nemar

Metadata: Complete (100%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import NM000329

dataset = NM000329(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 = NM000329(cache_dir="./data", subject="01")

Advanced query

dataset = NM000329(
    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{nm000329,
  title = {Brandl2020},
  author = {Stephanie Brandl and Benjamin Blankertz and Tobias Dahne},
  doi = {10.3389/fnins.2020.566147},
  url = {https://doi.org/10.3389/fnins.2020.566147},
}

About This Dataset#

Brandl2020

Motor Imagery under distraction dataset from Brandl and Blankertz 2020.

Dataset Overview

Code: Brandl2020 Paradigm: imagery DOI: 10.3389/fnins.2020.566147

View full README

Brandl2020

Motor Imagery under distraction dataset from Brandl and Blankertz 2020.

Dataset Overview

Code: Brandl2020 Paradigm: imagery DOI: 10.3389/fnins.2020.566147 Subjects: 16 Sessions per subject: 1 Events: left_hand=1, right_hand=2 Trial interval: [0, 4.5] s Runs per session: 7 File format: MAT (HDF5 v7.3)

Acquisition

Sampling rate: 1000.0 Hz Number of channels: 63 Channel types: eeg=63 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, FT7, FT8, Fp1, Fp2, Fpz, Fz, O1, O2, Oz, P1, P2, P3, P4, P5, P6, P7, P8, PO3, PO4, PO7, PO8, POz, Pz, T7, T8, TP10, TP7, TP8, TP9 Montage: standard_1005 Hardware: 2x BrainAmp (Brain Products) Software: BBCI Toolbox (MATLAB) Reference: nose Sensor type: Ag/AgCl wet Line frequency: 50.0 Hz Cap manufacturer: EasyCap Cap model: Fast’n Easy Cap

Participants

Number of subjects: 16 Health status: healthy Age: mean=26.3 Gender distribution: female=6, male=10 BCI experience: mostly naive (3/16 had prior BCI experience)

Experimental Protocol

Paradigm: imagery Number of classes: 2 Class labels: left_hand, right_hand Trial duration: 4.5 s Tasks: calibration, clean, eyesclosed, news, numbers, flicker, stimulation Study design: Motor imagery under distraction: 1 calibration run (no feedback, no distraction) + 6 feedback runs with different distraction conditions (clean, eyes closed, news, number search, flicker, vibro-tactile stimulation) Feedback type: auditory Stimulus type: auditory Stimulus modalities: auditory Primary modality: auditory Synchronicity: cue-based Mode: online Training/test split: False Instructions: Subjects received auditory cues (‘links’ for left, ‘rechts’ for right) and performed motor imagery of left or right hand movement

HED Event Annotations

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

     ├─ Sensory-event, Experimental-stimulus, Visual-presentation
     └─ Agent-action
        └─ Imagine
           ├─ Move
           └─ Left, Hand

right_hand

├─ Sensory-event, Experimental-stimulus, Visual-presentation
└─ Agent-action
   └─ Imagine
      ├─ Move
      └─ Right, Hand

Paradigm-Specific Parameters

Detected paradigm: motor_imagery Imagery tasks: left_hand, right_hand Imagery duration: 4.5 s

Data Structure

Trials: 504 Trials per class: left_hand=252, right_hand=252 Blocks per session: 7 Trials context: 7 runs per subject: 1 calibration (72 trials) + 6 feedback runs (72 trials each, 6 distraction conditions)

Preprocessing

Data state: raw Preprocessing applied: False

Signal Processing

Classifiers: CSP+LDA Feature extraction: CSP, bandpower Frequency bands: mu=[8.0, 13.0] Hz; beta=[13.0, 30.0] Hz Spatial filters: CSP

Cross-Validation

Method: holdout Evaluation type: within_subject

BCI Application

Applications: motor_control Environment: laboratory Online feedback: True

Tags

Pathology: Healthy Modality: Motor Type: Motor Imagery

Documentation

DOI: 10.3389/fnins.2020.566147 License: CC-BY-NC-ND-4.0 Investigators: Stephanie Brandl, Benjamin Blankertz, Tobias Dahne Senior author: Benjamin Blankertz Institution: Technische Universitaet Berlin Department: Department of Neurotechnology Country: DE Repository: DepositOnce TU Berlin Data URL: https://depositonce.tu-berlin.de/handle/11303/10934.2 Publication year: 2020 Funding: BMBF/BIFOLD (01IS18025A, 01IS18037A) Ethics approval: Approved by the ethics committee of the Charite University Medicine Berlin How to acknowledge: Please cite: Brandl, S. and Blankertz, B. (2020). Motor Imagery Under Distraction – An Open Access BCI Dataset. Frontiers in Neuroscience, 14, 566147. https://doi.org/10.3389/fnins.2020.566147 Keywords: brain-computer interface, motor imagery, EEG, distraction, open access, BCI

Abstract

We present an open-access dataset of a motor imagery brain-computer interface (BCI) experiment conducted under six different distraction conditions. Sixteen healthy participants performed left vs. right hand motor imagery while being distracted by flickering video, number search tasks, news listening, eyes closed, vibro-tactile stimulation, or no distraction. Each participant completed one calibration run without feedback and six feedback runs under the different distraction conditions, resulting in 504 trials per subject.

Methodology

Participants completed one session with 7 runs of 72 trials each. Run 1 was calibration (no feedback, no distraction). Runs 2-7 included auditory feedback and one of six distraction conditions. Auditory cues indicated left or right hand imagery. Trial duration was 4.5 s with 2.5 s ITI. Online classification used CSP with LDA. EEG recorded at 1000 Hz with 63 channels, nose reference, using two BrainAmp amplifiers.

References

Brandl, S. and Blankertz, B. (2020). Motor Imagery Under Distraction – An Open Access BCI Dataset. Frontiers in Neuroscience, 14, 566147. https://doi.org/10.3389/fnins.2020.566147 Notes .. versionadded:: 1.2.0 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	`NM000329`
Title	Brandl2020
Author (year)	`Brandl2020`
Canonical	—
Importable as	`NM000329`, `Brandl2020`
Year	2020
Authors	Stephanie Brandl, Benjamin Blankertz, Tobias Dahne
License	CC-BY-NC-ND-4.0
Citation / DOI	doi:10.3389/fnins.2020.566147
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: 16
Recordings: 112
Tasks: 1

Channels & sampling rate

Channels: 63
Sampling rate (Hz): 1000.0
Duration (hours): 97.11163555555557

API Reference#

Use the NM000329 class to access this dataset programmatically.

class eegdash.dataset.NM000329(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

Brandl2020

Study:: nm000329 (NeMAR)
Author (year):: Brandl2020
Canonical:: —

Also importable as: NM000329, Brandl2020.

Modality: eeg; Experiment type: Motor; Subject type: Healthy. Subjects: 16; recordings: 112; 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/nm000329 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000329 DOI: https://doi.org/10.3389/fnins.2020.566147

Examples

>>> from eegdash.dataset import NM000329
>>> dataset = NM000329(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