NM000122: eeg dataset, 12 subjects#

Chen2017 – Single-flicker online SSVEP BCI dataset

Access recordings and metadata through EEGDash.

Citation: Jingjing Chen, Dan Zhang, Andreas K. Engel, Qin Gong, Alexander Maye (2019). Chen2017 – Single-flicker online SSVEP BCI dataset.

Modality: eeg Subjects: 12 Recordings: 12 License: CC BY 4.0 Source: nemar

Metadata: Complete (90%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import NM000122

dataset = NM000122(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 = NM000122(cache_dir="./data", subject="01")

Advanced query

dataset = NM000122(
    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{nm000122,
  title = {Chen2017 – Single-flicker online SSVEP BCI dataset},
  author = {Jingjing Chen and Dan Zhang and Andreas K. Engel and Qin Gong and Alexander Maye},
}

About This Dataset#

Single-flicker online SSVEP BCI dataset

Single-flicker online SSVEP BCI dataset.

Dataset Overview

Code: Chen2017SingleFlicker
Paradigm: ssvep
DOI: 10.1371/journal.pone.0178385

View full README

Single-flicker online SSVEP BCI dataset

Single-flicker online SSVEP BCI dataset.

Dataset Overview

Code: Chen2017SingleFlicker
Paradigm: ssvep
DOI: 10.1371/journal.pone.0178385
Subjects: 12
Sessions per subject: 2
Events: north=1, east=2, west=3, south=4
Trial interval: [0.0, 3.5] s
File format: XDF/MAT

Acquisition

Sampling rate: 512.0 Hz
Number of channels: 32
Channel types: eeg=32
Montage: biosemi32
Hardware: BioSemi ActiveTwo
Reference: CMS/DRL
Sensor type: active
Line frequency: 50.0 Hz
Cap manufacturer: BioSemi
Electrode material: sintered Ag/AgCl

Participants

Number of subjects: 12
Health status: healthy
Age: mean=23.5, min=19, max=32
Gender distribution: male=5, female=7

Experimental Protocol

Paradigm: ssvep
Task type: spatial navigation
Number of classes: 4
Class labels: north, east, west, south
Study design: Spatial navigation with single 15 Hz flicker
Feedback type: visual
Stimulus type: single-flicker spatially coded
Stimulus modalities: visual
Primary modality: visual
Synchronicity: synchronous
Mode: online
Training/test split: True

HED Event Annotations

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

north

     ├─ Sensory-event
     ├─ Experimental-stimulus
     ├─ Visual-presentation
     └─ Label/north

east

     ├─ Sensory-event
     ├─ Experimental-stimulus
     ├─ Visual-presentation
     └─ Label/east

west

     ├─ Sensory-event
     ├─ Experimental-stimulus
     ├─ Visual-presentation
     └─ Label/west

south

├─ Sensory-event
├─ Experimental-stimulus
├─ Visual-presentation
└─ Label/south

Paradigm-Specific Parameters

Detected paradigm: ssvep
Stimulus frequencies: [15.0] Hz

Signal Processing

Classifiers: LDA
Feature extraction: CCA
Frequency bands: bandpass=[1.0, 80.0] Hz
Spatial filters: CCA

Cross-Validation

Evaluation type: within_subject

BCI Application

Applications: spatial_navigation
Environment: lab
Online feedback: True

Tags

Pathology: healthy
Modality: visual
Type: perception

Documentation

DOI: 10.1371/journal.pone.0178385
License: CC BY 4.0
Investigators: Jingjing Chen, Dan Zhang, Andreas K. Engel, Qin Gong, Alexander Maye
Senior author: Alexander Maye
Institution: University Medical Center Hamburg-Eppendorf
Department: Department of Neurophysiology and Pathophysiology, University Medical Center Hamburg-Eppendorf
Country: DE
Repository: Zenodo
Data URL: https://zenodo.org/records/580485
Publication year: 2017
Funding: DFG TRR169/B1/Z2 Crossmodal Learning; Landesforschungsfoerderung Hamburg CROSS FV25
Ethics approval: Ethics committee of the medical association, Hamburg
Keywords: SSVEP, BCI, spatial navigation, single-flicker, online BCI

References

J. Chen, D. Zhang, A. K. Engel, Q. Gong, and A. Maye, “Application of a single-flicker online SSVEP BCI for spatial navigation,” PLoS ONE, vol. 12, no. 5, e0178385, 2017. DOI: 10.1371/journal.pone.0178385 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 1.4.3 (Mother of All BCI Benchmarks) NeuroTechX/moabb

Dataset Information#

Dataset ID	`NM000122`
Title	Chen2017 – Single-flicker online SSVEP BCI dataset
Author (year)	`Chen2017`
Canonical	—
Importable as	`NM000122`, `Chen2017`
Year	2019
Authors	Jingjing Chen, Dan Zhang, Andreas K. Engel, Qin Gong, Alexander Maye
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: 12
Recordings: 12
Tasks: 1

Channels & sampling rate

Channels: 32
Sampling rate (Hz): 512.0
Duration (hours): 3.2708430989583333

Electrode Layout#

Electrode layout — EEG · 32 sensors — 32 channels

Dataset Statistics#

Age distribution (n=12, range 23–23 yr)

20

Channel counts: 32 ch (n=12 recordings)

Sampling frequencies: 512.0 Hz (n=12 recordings)

Total recording duration: 3 h 16 min

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

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.

Files: —

Size: —

Subjects: —

Click to load file structure…

API Reference#

Use the NM000122 class to access this dataset programmatically.

class eegdash.dataset.NM000122(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

Chen2017 – Single-flicker online SSVEP BCI dataset

Study:: nm000122 (NeMAR)
Author (year):: Chen2017
Canonical:: —

Also importable as: NM000122, Chen2017.

Modality: eeg; Experiment type: Perception; Subject type: Healthy. Subjects: 12; recordings: 12; 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/nm000122 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000122

Examples

>>> from eegdash.dataset import NM000122
>>> dataset = NM000122(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)