EEGdash›NeMAR›NM000144

Iss. 144 · 9 subjects · 18 recordings · CC-BY-NC-ND-4.0

Dataset Brief · BNCI 2015-004 Mental tasks dataset

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

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

9-participant EEG dataset — BNCI 2015-004 Mental tasks dataset.

Data & curation Reinhold Scherer · Josef Faller · Elisabeth V. C. Friedrich · Eloy Opisso · Ursula Costa · Andrea Kübler · …
Year 2017 · Distributed via NeMAR
Funding FP7 EU Research Projects BrainAble (No. 247447) · ABC (No. 287774) · + 1 more

EEG · 30 ch256 HzBIDS 1.9.0Task · imagery2 sessionsOtherVisualMotor

Layer 01Study

What was asked

Hypothesis, independent & dependent variables, paradigm, cohort, and the editorial caveats around what the recordings can and cannot answer.

Layer 02Signal · BIDS

What was recorded

Sidecars, channels & electrodes, coordinate system, event semantics, and quality stats from the NEMAR pipeline when available.

Layer 03Training · ML

What you can train on

Recommended access modes — MNE Raw, braindecode windows, PyTorch DataLoader — plus the targets the metadata makes addressable.

§ 01Access · Get started

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},
}

§ 02Study · The README

About This Dataset#

BNCI 2015-004 Mental tasks dataset.

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

DOI

BNCI 2015-004 Mental tasks dataset

math

View full README

DOI

BNCI 2015-004 Mental tasks dataset

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

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Age distribution by gender (n=9, range 38–38 yr, mean 38.0 yr)

35

Other · 9

Channel counts: 30 ch (n=18 recordings)

Sampling frequencies: 256.0 Hz (n=18 recordings)

Total recording duration: 13 h 45 min

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 30 ch · EEG · 256 Hz · 9 subjects, 18 recordings

Live trace viewer — sub-6 · ses-0 · task-imagery · run-0

Showing one representative recording out of 9 subjects and 18 recordings in this dataset. Browse the full set on OpenNeuro; drop any other _eeg.{set,edf,bdf,vhdr} file onto the viewer (or pass ?eeg=<url>) to inspect it.

Electrode layout — EEG · 30 sensors — 30 channels

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

§ 05Manifest · BIDS tree

Manifest#

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.

Recordings—

Files—

Subjects—

Modalities—

Click to load file structure…

§ 06API · Programmatic access

API Reference#

Signature

eegdash.dataset

class

eegdash.dataset.NM000144(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)Scherer2015

Canonical—

Importable asNM000144 · Scherer2015

Sourceeegdash/dataset/registry.py · [source ↗]

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

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)

Parameters:

path (str) –

Directory in which subdirectories are created to store
-raw.fif | -epo.fif and .json files to.
overwrite (bool) – Whether to delete old subdirectories that will be saved to in this call.
offset (int) – If provided, the integer is added to the id of the dataset in the concat. This is useful in the setting of very large datasets, where one dataset has to be processed and saved at a time to account for its original position.

Access modesMNE → braindecode → PyTorch → ML

.rawMNE Raw object — standard tools (filter, epoch, ICA, plot_psd).mne

BaseConcatDatasetEach record is a lazy BaseDataset from braindecode — windowed via create_windows_from_events.braindecode

DataLoaderWraps the windowed dataset into a PyTorch DataLoader; supports parallel workers and on-the-fly augmentations.pytorch

Zarr cacheOptional braindecode Zarr mirror for fast resume; persisted to cache_dir.zarr

Hugging FaceNo per-dataset mirror published yet — browse the EEGDash org listing for sibling datasets. See the datasets loader API.huggingface

Croissant 1.0Machine-readable JSON-LD descriptor — NM000144.croissant.json (MLCommons schema, ingestible by PyTorch / TensorFlow / JAX).mlcommons

Examples using EEGDashcurated · start here

Find datasets with the EEGDash APIQuery the catalogue, filter by task or modality, list candidates.

Load one EEG recordingResolve a single record to an MNE Raw with channels and events.

EEG recording to PyTorch DataLoaderWrap braindecode windows in a DataLoader for model training.

Preprocess EEG and create windowsFilter, resample, epoch — and persist the windowed dataset.

Save and reload prepared dataCache a windowed dataset to disk and reattach it without recompute.

Download a dataset locallyPrefetch BIDS files to a local cache and validate the layout.

Swap any load_dataset(...) call for nm000144 to reproduce the tutorial on this dataset.

Citation

Reinhold Scherer, Josef Faller, Elisabeth V. C. Friedrich, Eloy Opisso, Ursula Costa, … (2017). BNCI 2015-004 Mental tasks dataset. 10.82901/nemar.nm000144

Provenance

¹Contributed to nemar in BIDS format.

²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.

³Persistent identifier: 10.82901/nemar.nm000144.

Related & sibling datasets

NM000149EEG · 10 subj NM000170EEG · 10 subj NM000230EEG · 30 subj NM000137EEG · 7 subj NM000146EEG · 10 subj

+ 1 more — see See Also below →

BIDS

BIDS 1.9.0

Sidecars

events · events.json · channels · eeg.json

Provenance

CC-BY-NC-ND-4.0 · 10.82901/nemar.nm000144

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HF org

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

NM000144: eeg dataset, 9 subjects#

Quickstart#

About This Dataset#

Cohort#

Dataset Statistics#

Signal · Electrodes & live trace#

NEMAR Processing Statistics#

Manifest#

File Explorer#

API Reference#

Citation

Provenance

Related & sibling datasets

See Also#