NM000144: eeg dataset, 9 subjects#

BNCI 2015-004 Mental tasks dataset

Access recordings and metadata through EEGDash.

Citation: Reinhold Scherer, Josef Faller, Elisabeth V. C. Friedrich, Eloy Opisso, Ursula Costa, Andrea Kübler, Gernot R. Müller-Putz (2017). BNCI 2015-004 Mental tasks dataset.

Modality: eeg Subjects: 9 Recordings: 18 License: CC-BY-NC-ND-4.0 Source: nemar

Metadata: Complete (90%)

Quickstart#

Install

pip install eegdash

Access the data

from eegdash.dataset import NM000144

dataset = NM000144(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Filter by subject

dataset = NM000144(cache_dir="./data", subject="01")

Advanced query

dataset = NM000144(
    cache_dir="./data",
    query={"subject": {"$in": ["01", "02"]}},
)

Iterate recordings

for rec in dataset:
    print(rec.subject, rec.raw.info['sfreq'])

If you use this dataset in your research, please cite the original authors.

BibTeX

@dataset{nm000144,
  title = {BNCI 2015-004 Mental tasks dataset},
  author = {Reinhold Scherer and Josef Faller and Elisabeth V. C. Friedrich and Eloy Opisso and Ursula Costa and Andrea Kübler and Gernot R. Müller-Putz},
}

About This Dataset#

BNCI 2015-004 Mental tasks dataset

BNCI 2015-004 Mental tasks dataset.

Dataset Overview

  • Code: BNCI2015-004

  • Paradigm: imagery

  • DOI: 10.1371/journal.pone.0123727

View full README

BNCI 2015-004 Mental tasks dataset

BNCI 2015-004 Mental tasks dataset.

Dataset Overview

  • Code: BNCI2015-004

  • Paradigm: imagery

  • DOI: 10.1371/journal.pone.0123727

  • Subjects: 9

  • Sessions per subject: 2

  • Events: math=1, letter=2, rotation=3, count=4, baseline=5

  • Trial interval: [0, 4] s

  • File format: gdf

  • Data preprocessed: True

Acquisition

  • Sampling rate: 256.0 Hz

  • Number of channels: 30

  • Channel types: eeg=30

  • Channel names: AFz, F7, F3, Fz, F4, F8, FC3, FCz, FC4, T3, C3, Cz, C4, T4, CP3, CPz, CP4, P7, P5, P3, P1, Pz, P2, P4, P6, P8, PO3, PO4, O1, O2

  • Montage: 10-20

  • Hardware: g.tec

  • Reference: left and right mastoid

  • Ground: left and right mastoid

  • Sensor type: active electrode

  • Line frequency: 50.0 Hz

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

  • Cap manufacturer: g.tec

  • Electrode type: g.LADYbird active electrodes

  • Auxiliary channels: EOG (2 ch, horizontal, vertical)

Participants

  • Number of subjects: 9

  • Health status: CNS tissue damage

  • Clinical population: stroke and spinal cord injury

  • Age: mean=38.0, std=10.0, min=20, max=57

  • Gender distribution: male=2, female=7

  • Handedness: not specified

  • BCI experience: naive

  • Species: human

Experimental Protocol

  • Paradigm: imagery

  • Number of classes: 5

  • Class labels: math, letter, rotation, count, baseline

  • Trial duration: 11.0 s

  • Tasks: word_association, mental_subtraction, spatial_navigation, right_hand_imagery, feet_imagery

  • Study design: Five mental tasks: word association (WORD), mental subtraction (SUB), spatial navigation (NAV), motor imagery of right hand (HAND), and motor imagery of both feet (FEET). Cue-guided paradigm with 7 seconds of continuous mental imagery per trial.

  • Feedback type: none

  • Stimulus type: visual cue

  • Stimulus modalities: visual

  • Primary modality: visual

  • Synchronicity: synchronous

  • Mode: screening

  • Instructions: Participants were asked to continuously perform the specified mental imagery task for 7 seconds. For MI: kinesthetic imagination of movement (e.g., squeezing a rubber ball for hand, dorsiflexion for feet). For WORD: generate words beginning with presented letter. For SUB: successive elementary subtractions. For NAV: spatial navigation.

HED Event Annotations

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

math

     ├─ Sensory-event, Experimental-stimulus, Visual-presentation
     └─ Agent-action
        └─ Imagine
           ├─ Think
           └─ Label/math

letter

     ├─ Sensory-event, Experimental-stimulus, Visual-presentation
     └─ Agent-action
        └─ Imagine
           ├─ Think
           └─ Label/letter

rotation

     ├─ Sensory-event, Experimental-stimulus, Visual-presentation
     └─ Agent-action
        └─ Imagine
           ├─ Think
           └─ Label/rotation

count

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

baseline

├─ Sensory-event
├─ Experimental-stimulus
├─ Visual-presentation
└─ Rest

Paradigm-Specific Parameters

  • Detected paradigm: motor_imagery

  • Imagery tasks: right_hand, feet, word_association, mental_subtraction, spatial_navigation

  • Cue duration: 1.0 s

  • Imagery duration: 7.0 s

Data Structure

  • Trials: 40

  • Blocks per session: 8

  • Trials context: per_class_per_day

Preprocessing

  • Data state: filtered

  • Preprocessing applied: True

  • Steps: bandpass filter, notch filter, artifact rejection

  • 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] Hz

  • Artifact methods: manual artifact rejection based on EOG

  • Re-reference: left and right mastoid

Signal Processing

  • Classifiers: LDA

  • Feature extraction: bandpower, temporal features

  • Frequency bands: mu=[8, 12] Hz; beta=[13, 30] Hz

Cross-Validation

  • Method: 10-fold cross-validation

  • Folds: 10

  • Evaluation type: within_session, cross_session

Performance (Original Study)

  • Accuracy: 77.0%

  • Best Task Pair Gmac: 77.0

  • Sub Vs Feet Gmac: 77.0

  • Word Vs Hand Gmac: 70.0

  • Hand Vs Feet Gmac: 64.0

  • Between Day Word Vs Hand Gmac: 82.0

BCI Application

  • Applications: communication, motor_function_restoration

  • Environment: rehabilitation center

  • Online feedback: False

Tags

  • Pathology: Stroke, Spinal Cord Injury, CNS Damage

  • Modality: Motor, Cognitive

  • Type: Motor, Cognitive

Documentation

  • DOI: 10.1371/journal.pone.0123727

  • License: CC-BY-NC-ND-4.0

  • Investigators: Reinhold Scherer, Josef Faller, Elisabeth V. C. Friedrich, Eloy Opisso, Ursula Costa, Andrea Kübler, Gernot R. Müller-Putz

  • Senior author: Reinhold Scherer

  • Contact: reinhold.scherer@tugraz.at

  • Institution: Institut Guttmann

  • Department: Institut Universitari de Neurorehabilitació adscrit a la UAB

  • Address: 08916 Badalona, Barcelona, Spain

  • Country: Spain

  • Repository: BNCI Horizon 2020

  • Data URL: https://bnci-horizon-2020.eu/database/data-sets

  • Publication year: 2015

  • Funding: FP7 EU Research Projects BrainAble (No. 247447); ABC (No. 287774); BackHome (No. 288566)

  • Ethics approval: Comitè d’Ètica Assistencial de l’Institut Guttman

  • Keywords: brain-computer interface, motor imagery, mental tasks, EEG, CNS tissue damage, stroke, spinal cord injury, binary classification

References

Zhang, X., Yao, L., Zhang, Q., Kanhere, S., Sheng, M., & Liu, Y. (2017). A survey on deep learning based brain computer interface: Recent advances and new frontiers. IEEE Transactions on Cognitive and Developmental Systems, 10(2), 145-163. Notes .. note:: BNCI2015_004 was previously named BNCI2015004. BNCI2015004 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 1.5.0 (Mother of All BCI Benchmarks) https://github.com/NeuroTechX/moabb

Dataset Information#

Dataset ID

NM000144

Title

BNCI 2015-004 Mental tasks dataset

Author (year)

Scherer2015

Canonical

BNCI2015

Importable as

NM000144, Scherer2015, BNCI2015

Year

2017

Authors

Reinhold Scherer, Josef Faller, Elisabeth V. C. Friedrich, Eloy Opisso, Ursula Costa, Andrea Kübler, Gernot R. Müller-Putz

License

CC-BY-NC-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: 9

  • Recordings: 18

  • Tasks: 1

Channels & sampling rate

  • Channels: 30

  • Sampling rate (Hz): 256.0

  • Duration (hours): 13.750518663194445

Tags

  • Pathology: Other

  • Modality: Visual

  • Type: Motor

Files & format

  • Size on disk: 1.1 GB

  • File count: 18

  • Format: BIDS

License & citation

  • License: CC-BY-NC-ND-4.0

  • DOI: —

Provenance

API Reference#

Use the NM000144 class to access this dataset programmatically.

class eegdash.dataset.NM000144(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

BNCI 2015-004 Mental tasks dataset

Study:

nm000144 (NeMAR)

Author (year):

Scherer2015

Canonical:

—

Also importable as: NM000144, Scherer2015.

Modality: eeg; Experiment type: Motor; Subject type: Other. Subjects: 9; recordings: 18; 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/nm000144 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000144

Examples

>>> from eegdash.dataset import NM000144
>>> dataset = NM000144(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

See Also#