NM000140: eeg dataset, 12 subjects#

Name: BNCI 2015-001 Motor Imagery dataset
Published: 2012-01-01
License: CC-BY-NC-ND-4.0

BNCI 2015-001 Motor Imagery dataset

Access recordings and metadata through EEGDash.

Citation: Josef Faller, Carmen Vidaurre, Teodoro Solis-Escalante, Christa Neuper, Reinhold Scherer (2012). BNCI 2015-001 Motor Imagery dataset. 10.82901/nemar.nm000140

Modality: eeg Subjects: 12 Recordings: 28 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 NM000140

dataset = NM000140(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 = NM000140(cache_dir="./data", subject="01")

Advanced query

dataset = NM000140(
    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{nm000140,
  title = {BNCI 2015-001 Motor Imagery dataset},
  author = {Josef Faller and Carmen Vidaurre and Teodoro Solis-Escalante and Christa Neuper and Reinhold Scherer},
  doi = {10.82901/nemar.nm000140},
  url = {https://doi.org/10.82901/nemar.nm000140},
}

About This Dataset#

DOI

BNCI 2015-001 Motor Imagery dataset

BNCI 2015-001 Motor Imagery dataset.

Dataset Overview

Code: BNCI2015-001
Paradigm: imagery

View full README

DOI

BNCI 2015-001 Motor Imagery dataset

BNCI 2015-001 Motor Imagery dataset.

Dataset Overview

Code: BNCI2015-001
Paradigm: imagery
DOI: 10.1109/tnsre.2012.2189584
Subjects: 12
Sessions per subject: 2
Events: right_hand=1, feet=2
Trial interval: [0, 5] s
File format: gdf
Data preprocessed: True

Acquisition

Sampling rate: 512.0 Hz
Number of channels: 13
Channel types: eeg=13
Channel names: FC3, FCz, FC4, C5, C3, C1, Cz, C2, C4, C6, CP3, CPz, CP4
Montage: 10-20
Hardware: g.tec
Software: Matlab
Reference: Car
Sensor type: active electrode
Line frequency: 50.0 Hz
Online filters: 50 Hz notch
Cap manufacturer: g.tec
Cap model: g.GAMMAsys
Auxiliary channels: gsr

Participants

Number of subjects: 12
Health status: healthy
Age: mean=24.8
Gender distribution: male=7, female=5
Handedness: all right-handed
BCI experience: naive
Species: human

Experimental Protocol

Paradigm: imagery
Number of classes: 2
Class labels: right_hand, feet
Trial duration: 11.0 s
Study design: Two-class motor imagery: sustained right hand movement imagery (palmar grip) versus both feet movement imagery (plantar extension)
Feedback type: visual
Stimulus type: cursor_feedback
Stimulus modalities: visual, auditory
Primary modality: visual
Synchronicity: synchronous
Mode: training
Instructions: Relax during reference period (3s), perform sustained kinesthetic movement imagery during activity period. Condition 1 (arrow right): imagine palmar grip with right hand. Condition 2 (arrow down): imagine plantar extension of both feet.

HED Event Annotations

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

right_hand

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

feet

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

Paradigm-Specific Parameters

Detected paradigm: motor_imagery
Imagery tasks: right_hand_palmar_grip, both_feet_plantar_extension
Cue duration: 1.25 s
Imagery duration: 4.0 s

Data Structure

Trials: 200
Trials per class: right_hand=100, feet=100
Trials context: per_session

Preprocessing

Data state: filtered
Preprocessing applied: True
Steps: bandpass filter, notch filter
Highpass filter: 0.5 Hz
Lowpass filter: 100.0 Hz
Bandpass filter: {‘low_cutoff_hz’: 0.5, ‘high_cutoff_hz’: 100.0}
Notch filter: [50.0] Hz
Re-reference: car

Signal Processing

Classifiers: LDA
Feature extraction: logarithmic bandpower, CSP
Frequency bands: alpha=[10, 13] Hz; beta=[16, 24] Hz

Cross-Validation

Method: leave-one-out
Evaluation type: cross_session

Performance (Original Study)

Accuracy: 80.0%

BCI Application

Applications: communication, control
Online feedback: True

Tags

Pathology: Healthy
Modality: Motor
Type: Motor

Documentation

DOI: 10.1109/tnsre.2012.2189584
License: CC-BY-NC-ND-4.0
Investigators: Josef Faller, Carmen Vidaurre, Teodoro Solis-Escalante, Christa Neuper, Reinhold Scherer
Senior author: Reinhold Scherer
Contact: josef.faller@tugraz.at; christa.neuper@uni-graz.at; carmen.vidaurre@tu-berlin.de
Institution: Graz University of Technology
Department: Institute of Knowledge Discovery
Address: 8010 Graz, Austria
Country: Austria
Repository: BNCI Horizon
Publication year: 2012
Funding: FP7 Framework EU Research Project BrainAble (No. 247447)

References

Faller, J., Vidaurre, C., Solis-Escalante, T., Neuper, C., & Scherer, R. (2012). Autocalibration and recurrent adaptation: Towards a plug and play online ERD-BCI. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 20(3), 313-319. https://doi.org/10.1109/tnsre.2012.2189584 Notes .. note:: BNCI2015_001 was previously named BNCI2015001. BNCI2015001 will be removed in version 1.1. .. versionadded:: 0.4.0 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 1.4.3 (Mother of All BCI Benchmarks) NeuroTechX/moabb

Dataset Information#

Dataset ID	`NM000140`
Title	BNCI 2015-001 Motor Imagery dataset
Author (year)	`Faller2015`
Canonical	—
Importable as	`NM000140`, `Faller2015`
Year	2012
Authors	Josef Faller, Carmen Vidaurre, Teodoro Solis-Escalante, Christa Neuper, Reinhold Scherer
License	CC-BY-NC-ND-4.0
Citation / DOI	10.82901/nemar.nm000140
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: 28
Tasks: 1

Channels & sampling rate

Channels: 13
Sampling rate (Hz): 512.0
Duration (hours): 16.68931640625

Electrode Layout#

Electrode layout — EEG · 13 sensors — 13 channels

Dataset Statistics#

Age distribution (n=12, range 24–24 yr)

20

Channel counts: 13 ch (n=28 recordings)

Sampling frequencies: 512.0 Hz (n=28 recordings)

Total recording duration: 16 h 41 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 NM000140 class to access this dataset programmatically.

class eegdash.dataset.NM000140(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

BNCI 2015-001 Motor Imagery dataset

Study:: nm000140 (NeMAR)
Author (year):: Faller2015
Canonical:: —

Also importable as: NM000140, Faller2015.

Modality: eeg; Experiment type: Motor; Subject type: Healthy. Subjects: 12; recordings: 28; 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/nm000140 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000140 DOI: https://doi.org/10.82901/nemar.nm000140

Examples

>>> from eegdash.dataset import NM000140
>>> dataset = NM000140(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)