NM000135: eeg dataset, 1 subjects

BNCI 2014-004 Motor Imagery dataset

Access recordings and metadata through EEGDash.

Citation: R. Leeb, C. Brunner, G. R. Müller-Putz, A. Schlögl, G. Pfurtscheller, F. Lee, C. Keinrath, R. Scherer, H. Bischof (2019). BNCI 2014-004 Motor Imagery dataset.

Modality: eeg Subjects: 1 Recordings: 5 License: CC-BY-ND-4.0 Source: nemar

Metadata: Complete (90%)

Quickstart

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import NM000135

dataset = NM000135(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 = NM000135(cache_dir="./data", subject="01")

Advanced query

dataset = NM000135(
    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{nm000135,
  title = {BNCI 2014-004 Motor Imagery dataset},
  author = {R. Leeb and C. Brunner and G. R. Müller-Putz and A. Schlögl and G. Pfurtscheller and F. Lee and C. Keinrath and R. Scherer and H. Bischof},
}

About This Dataset

BNCI 2014-004 Motor Imagery dataset

BNCI 2014-004 Motor Imagery dataset.

Dataset Overview

• Code: BNCI2014-004

• Paradigm: imagery

• DOI: 10.1109/TNSRE.2007.906956

View full README

BNCI 2014-004 Motor Imagery dataset

BNCI 2014-004 Motor Imagery dataset.

Dataset Overview

• Code: BNCI2014-004

• Paradigm: imagery

• DOI: 10.1109/TNSRE.2007.906956

• Subjects: 9

• Sessions per subject: 5

• Events: left_hand=1, right_hand=2

• Trial interval: [3, 7.5] s

• Session IDs: 01T, 02T, 03T, 04E, 05E

• File format: GDF

Acquisition

• Sampling rate: 250.0 Hz

• Number of channels: 3

• Channel types: eeg=3, eog=3

• Channel names: C3, C4, Cz, EOG1, EOG2, EOG3

• Montage: standard_1020

• Hardware: g.tec

• Software: rtsBCI (MATLAB/Simulink)

• Reference: left mastoid

• Ground: Fz

• Sensor type: EEG

• Line frequency: 50.0 Hz

• Online filters: 0.5-100 Hz bandpass, 50 Hz notch

• Cap manufacturer: Easycap

• Electrode material: Ag/AgCl

• Auxiliary channels: EOG (3 ch, horizontal, vertical, radial)

Participants

• Number of subjects: 9

• Health status: healthy

• Age: mean=24.7, std=3.3

• Handedness: right

• BCI experience: naive

• Species: human

Experimental Protocol

• Paradigm: imagery

• Task type: motor_imagery

• Number of classes: 2

• Class labels: left_hand, right_hand

• Trial duration: 7.5 s

• Tasks: left_hand_imagery, right_hand_imagery

• Study design: Two-class motor imagery: left hand and right hand. Screening sessions (01T, 02T) without feedback, feedback sessions (03T, 04E, 05E) with smiley feedback.

• Study domain: brain-computer interface

• Feedback type: visual

• Stimulus type: arrow_cue

• Stimulus modalities: visual, auditory

• Primary modality: visual

• Synchronicity: cue_based

• Mode: both

• Training/test split: True

• Instructions: Subjects selected their best motor imagery strategy (e.g., squeezing a ball or pulling a brake) and performed kinesthetic motor imagery of left or right hand movements.

HED Event Annotations

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

left_hand

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

right_hand

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

Paradigm-Specific Parameters

• Detected paradigm: motor_imagery

• Imagery tasks: left_hand, right_hand

• Cue duration: 1.25 s

• Imagery duration: 4.0 s

Data Structure

• Trials: {‘screening’: 120, ‘feedback’: 160}

• Trials context: per session

Preprocessing

• Data state: raw with online filtering

• Preprocessing applied: True

• Steps: bandpass filtering, notch filtering

• 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

• Filter type: analog

• Notes: Online bandpass (0.5-100 Hz) and notch (50 Hz) filters applied during recording. Artifact trials marked with event type 1023. EOG channels provided for user-applied artifact correction.

Signal Processing

• Classifiers: LDA

• Feature extraction: Bandpower, BP

Cross-Validation

• Method: 10x10 cross-validation

• Folds: 10

• Evaluation type: within_subject

BCI Application

• Applications: motor_control

• Environment: laboratory

• Online feedback: True

Tags

• Pathology: Healthy

• Modality: Motor

• Type: Motor Imagery

Documentation

• Description: BCI Competition 2008 - Graz data set B: Two-class motor imagery dataset (left/right hand) with screening sessions (no feedback) and smiley feedback sessions. 9 subjects, 3 bipolar EEG channels (C3, Cz, C4) + 3 EOG channels, 250 Hz.

• DOI: 10.1109/TNSRE.2007.906956

• License: CC-BY-ND-4.0

• Investigators: R. Leeb, C. Brunner, G. R. Müller-Putz, A. Schlögl, G. Pfurtscheller, F. Lee, C. Keinrath, R. Scherer, H. Bischof

• Senior author: G. Pfurtscheller

• Institution: Graz University of Technology

• Department: Institute for Knowledge Discovery

• Country: AT

• Repository: BNCI Horizon

• Data URL: http://biosig.sourceforge.net/

• Publication year: 2007

• Keywords: brain-computer interface, BCI, electroencephalogram, EEG, motor imagery, BCI competition, smiley feedback

External Links

• Source: http://biosig.sourceforge.net/

Abstract

BCI Competition 2008 Graz data set B. EEG data from 9 subjects performing two-class motor imagery (left hand vs right hand). Two screening sessions without feedback (120 trials each) and three feedback sessions with smiley feedback (160 trials each). Three bipolar EEG channels (C3, Cz, C4) and three EOG channels recorded at 250 Hz.

Methodology

Subjects performed kinesthetic motor imagery of left or right hand movements. Two screening sessions (01T, 02T) without feedback: 6 runs x 20 trials = 120 trials per session. Three feedback sessions (03T, 04E, 05E) with smiley feedback: 4 runs x 40 trials (20 per class) = 160 trials per session. Screening trials: fixation cross + beep at t=0, arrow cue at ~t=2 for 1.25s, imagery for 4s, break. Feedback trials: smiley at t=0, beep at t=2, cue from t=3 to t=7.5 with continuous smiley feedback. Three bipolar EEG channels (C3, Cz, C4) plus three monopolar EOG channels recorded at 250 Hz with 0.5-100 Hz bandpass and 50 Hz notch filter. EEG ground at Fz, EOG reference at left mastoid. Amplifier: g.tec. Software: rtsBCI (MATLAB/Simulink).

References

Tangermann, M., Muller, K.R., Aertsen, A., Birbaumer, N., Braun, C., Brunner, C., Leeb, R., Mehring, C., Miller, K.J., Mueller-Putz, G. and Nolte, G., 2012. Review of the BCI competition IV. Frontiers in neuroscience, 6, p.55. Notes .. note:: BNCI2014_004 was previously named BNCI2014004. BNCI2014004 will be removed in version 1.1. .. versionadded:: 0.4.0 This dataset is commonly referred to as “BCI Competition IV Dataset 2b”. It is widely used for binary motor imagery classification tasks. See Also BNCI2014_001 : 4-class motor imagery (Dataset 2a) BNCI2014_002 : 2-class motor imagery with Laplacian derivations 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) https://github.com/NeuroTechX/moabb

Dataset Information

Dataset ID	NM000135
Title	BNCI 2014-004 Motor Imagery dataset
Author (year)	Leeb2014
Canonical	BNCI2014004
Importable as	NM000135, Leeb2014, BNCI2014004
Year	2019
Authors	Leeb, C. Brunner, G. R. Müller-Putz, A. Schlögl, G. Pfurtscheller, F. Lee, C. Keinrath, R. Scherer, H. Bischof
License	CC-BY-ND-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: 1

• Recordings: 5

• Tasks: 1

Channels & sampling rate

• Channels: 3

• Sampling rate (Hz): 250.0

• Duration (hours): 2.8521533333333333

Tags

• Pathology: Healthy

• Modality: Visual

• Type: Motor

Files & format

• Size on disk: 22.6 MB

• File count: 5

• Format: BIDS

License & citation

• License: CC-BY-ND-4.0

• DOI: —

Provenance

• Source: nemar

• OpenNeuro: nm000135

• NeMAR: nm000135

API Reference

Use the NM000135 class to access this dataset programmatically.

class eegdash.dataset.NM000135(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

Bases: EEGDashDataset

BNCI 2014-004 Motor Imagery dataset

Study:

nm000135 (NeMAR)

Author (year):

Leeb2014

Canonical:

BNCI2014004

Also importable as: NM000135, Leeb2014, BNCI2014004.

Modality: eeg; Experiment type: Motor; Subject type: Healthy. Subjects: 1; recordings: 5; 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/nm000135 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000135

Examples

>>> from eegdash.dataset import NM000135
>>> dataset = NM000135(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

__init__(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)

save(path, overwrite=False)

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

See Also

• eegdash.dataset.EEGDashDataset

• eegdash.dataset

• OpenNeuro dataset page

• NeMAR dataset page

• eegdash.dataset.DS001785

• eegdash.dataset.DS001787

• eegdash.dataset.DS001810

• eegdash.dataset.DS001849

• eegdash.dataset.DS001971