NM000144: eeg dataset, 9 subjects#

Name: BNCI 2015-004 Mental tasks dataset
Published: 2017-01-01
License: CC-BY-NC-ND-4.0

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. 10.82901/nemar.nm000144

Modality: eeg Subjects: 9 Recordings: 18 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 NM000144

dataset = NM000144(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 = 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'])

Cite This Dataset

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},
  doi = {10.82901/nemar.nm000144},
  url = {https://doi.org/10.82901/nemar.nm000144},
}

About This Dataset#

DOI

BNCI 2015-004 Mental tasks dataset

BNCI 2015-004 Mental tasks dataset.

Dataset Overview

Code: BNCI2015-004
Paradigm: imagery

View full README

DOI

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) NeuroTechX/moabb

Dataset Information#

Dataset ID	`NM000144`
Title	BNCI 2015-004 Mental tasks dataset
Author (year)	`Scherer2015`
Canonical	—
Importable as	`NM000144`, `Scherer2015`
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	10.82901/nemar.nm000144
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

Electrode Layout#

Electrode layout — EEG · 30 sensors — 30 channels

Dataset Statistics#

Age distribution (n=9, range 38–38 yr)

35

Channel counts: 30 ch (n=18 recordings)

Sampling frequencies: 256.0 Hz (n=18 recordings)

Total recording duration: 13 h 45 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 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 DOI: https://doi.org/10.82901/nemar.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: 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)