EEGdash›OpenNeuro›DS004514

Iss. 4514 · 12 subjects · 24 recordings · CC0

Dataset Brief · Simultaneous EEG and fNIRS recordings for semantic decoding o…

DS004514: eeg, fnirs dataset, 12 subjects#

Simultaneous EEG and fNIRS recordings for semantic decoding of imagined animals and tools

Citation: Milan Rybář, Riccardo Poli, Ian Daly (20). Simultaneous EEG and fNIRS recordings for semantic decoding of imagined animals and tools. 10.18112/openneuro.ds004514.v1.1.2

12-participant EEG, fNIRS dataset — Simultaneous EEG and fNIRS recordings for semantic decoding of imagined animals and tools.

Data & curation Milan Rybář · Riccardo Poli · Ian Daly
Year 20 · Distributed via OpenNeuro

EEG, fNIRS · 80 (12), 28 (6), 22 (6) ch8, 9, 2048 HzBIDS 1.7.02 tasksHealthyMultisensoryOther

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 DS004514

dataset = DS004514(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 = DS004514(cache_dir="./data", subject="01")

Advanced query

dataset = DS004514(
    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{ds004514,
  title = {Simultaneous EEG and fNIRS recordings for semantic decoding of imagined animals and tools},
  author = {Milan Rybář and Riccardo Poli and Ian Daly},
  doi = {10.18112/openneuro.ds004514.v1.1.2},
  url = {https://doi.org/10.18112/openneuro.ds004514.v1.1.2},
}

§ 02Study · The README

About This Dataset#

This dataset contains simultaneous electroencephalography (EEG) and near-infrared spectroscopy (fNIRS) signals recorded from 12 participants while performing a silent naming task and three sensory-based imagery tasks using visual, auditory, and tactile perception.

Participants were asked to visualize an object in their minds, imagine the sounds made by the object, and imagine the feeling of touching the object.

Description

EEG

EEG data were acquired with a BioSemi ActiveTwo system with 64 electrodes positioned according to the international 10-20 system, plus one electrode on each earlobe as references (‘EXG1’ channel is the left ear electrode and ‘EXG2’ channel is the right ear electrode).

Additionally, 2 electrodes placed on the left hand measured galvanic skin response (‘GSR1’ channel) and a respiration belt around the waist measured respiration (‘Resp’ channel). The sampling rate was 2048 Hz.

The electrode names were saved in a default BioSemi labeling scheme (A1-A32, B1-B32). See the Biosemi documentation for the corresponding international 10-20 naming scheme (https://www.biosemi.com/pics/cap_64_layout_medium.jpg, https://www.biosemi.com/headcap.htm).

View full README

Description

EEG

EEG data were acquired with a BioSemi ActiveTwo system with 64 electrodes positioned according to the international 10-20 system, plus one electrode on each earlobe as references (‘EXG1’ channel is the left ear electrode and ‘EXG2’ channel is the right ear electrode).

Additionally, 2 electrodes placed on the left hand measured galvanic skin response (‘GSR1’ channel) and a respiration belt around the waist measured respiration (‘Resp’ channel). The sampling rate was 2048 Hz.

The electrode names were saved in a default BioSemi labeling scheme (A1-A32, B1-B32). See the Biosemi documentation for the corresponding international 10-20 naming scheme (https://www.biosemi.com/pics/cap_64_layout_medium.jpg, https://www.biosemi.com/headcap.htm). For convenience, the following ordered channels

['A1', 'A2', 'A3', 'A4', 'A5', 'A6', 'A7', 'A8', 'A9', 'A10', 'A11', 'A12', 'A13', 'A14', 'A15', 'A16', 'A17', 'A18', 'A19', 'A20', 'A21', 'A22', 'A23', 'A24', 'A25', 'A26', 'A27', 'A28', 'A29', 'A30', 'A31', 'A32', 'B1', 'B2', 'B3', 'B4', 'B5', 'B6', 'B7', 'B8', 'B9', 'B10', 'B11', 'B12', 'B13', 'B14', 'B15', 'B16', 'B17', 'B18', 'B19', 'B20', 'B21', 'B22', 'B23', 'B24', 'B25', 'B26', 'B27', 'B28', 'B29', 'B30', 'B31', 'B32']

can thus be renamed to

['Fp1', 'AF7', 'AF3', 'F1', 'F3', 'F5', 'F7', 'FT7', 'FC5', 'FC3', 'FC1', 'C1', 'C3', 'C5', 'T7', 'TP7', 'CP5', 'CP3', 'CP1', 'P1', 'P3', 'P5', 'P7', 'P9', 'PO7', 'PO3', 'O1', 'Iz', 'Oz', 'POz', 'Pz', 'CPz', 'Fpz', 'Fp2', 'AF8', 'AF4', 'AFz', 'Fz', 'F2', 'F4', 'F6', 'F8', 'FT8', 'FC6', 'FC4', 'FC2', 'FCz', 'Cz', 'C2', 'C4', 'C6', 'T8', 'TP8', 'CP6', 'CP4', 'CP2', 'P2', 'P4', 'P6', 'P8', 'P10', 'PO8', 'PO4', 'O2']

fNIRS

fNIRS data were acquired with a NIRx NIRScoutXP continuous wave imaging system equipped with 4 light detectors, 8 light emitters (sources), and low-profile fNIRS optodes.

Both electrodes and optodes were placed in a NIRx NIRScap for integrated fNIRS-EEG layouts. Two different montages were used: frontal and temporal, see references for more information.

Stimulus

Folder ‘stimuli’ contains all images of the semantic categories of animals and tools presented to participants.

Example code

We have prepared example scripts to demonstrate how to load the EEG and fNIRS data into Python using MNE and MNE-BIDS packages. These scripts are located in the ‘code’ directory.

References

This dataset was analyzed in the following publications: [1] Rybář, M., Poli, R. and Daly, I., 2024. Using data from cue presentations results in grossly overestimating semantic BCI performance. Scientific Reports, 14(1), p.28003. [2] Rybář, M., Poli, R. and Daly, I., 2021. Decoding of semantic categories of imagined concepts of animals and tools in fNIRS. Journal of Neural Engineering, 18(4), p.046035. [3] Rybář, M., 2023. Towards EEG/fNIRS-based semantic brain-computer interfacing (Doctoral dissertation, University of Essex).

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Age distribution by gender (n=12, range 20–57 yr, mean 32.8 yr)

202530455055

Female · 9Male · 3

Sex composition

Female
9
Male
3
F : M ratio
3.00 : 1

75% female · n = 12 subjects with reported sex.

HandednessRight · 12

Channel counts (ch)

222880

Sampling frequencies (Hz)

7.88.92048

Total recording duration: 29 h

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 80 (12), 28 (6), 22 (6) ch · EEG, fNIRS · 8, 9, 2048 Hz · 12 subjects, 24 recordings

Live trace viewer — sub-12 · task-eeg

Showing one representative recording out of 12 subjects and 24 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 · 64 sensors — 64 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.DS004514(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)Rybar2023_Simultaneous

Canonical—

Importable asDS004514 · Rybar2023_Simultaneous

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

class eegdash.dataset.DS004514(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Simultaneous EEG and fNIRS recordings for semantic decoding of imagined animals and tools

Study:: ds004514 (OpenNeuro)
Author (year):: Rybar2023_Simultaneous
Canonical:: —

Also importable as: DS004514, Rybar2023_Simultaneous.

Modality: eeg, fnirs; Experiment type: Other; Subject type: Healthy. Subjects: 12; recordings: 24; tasks: 2.

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/ds004514 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004514 DOI: https://doi.org/10.18112/openneuro.ds004514.v1.1.2

Examples

>>> from eegdash.dataset import DS004514
>>> dataset = DS004514(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 FacePre-bundled mirror at EEGDash/ds004514 · pull with datasets.load_dataset("EEGDash/ds004514").huggingface

Croissant 1.0Machine-readable JSON-LD descriptor — DS004514.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 ds004514 to reproduce the tutorial on this dataset.

Citation

Milan Rybář, Riccardo Poli, Ian Daly (20). Simultaneous EEG and fNIRS recordings for semantic decoding of imagined animals and tools. 10.18112/openneuro.ds004514.v1.1.2

Provenance

¹Contributed to openneuro in BIDS format.

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

³Persistent identifier: 10.18112/openneuro.ds004514.v1.1.2.

Related & sibling datasets

DS007554EEG, FNIRS · 30 subj DS004107MEG · 9 subj DS006018EEG · 127 subj DS005752MEG · 123 subj DS005863EEG · 127 subj

+ 1 more — see See Also below →

BIDS

BIDS 1.7.0

Sidecars

events · events.json · channels · eeg.json

Provenance

CC0 · 10.18112/openneuro.ds004514.v1.1.2

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HuggingFace

Dataset ID	`DS004514`
Title	Simultaneous EEG and fNIRS recordings for semantic decoding of imagined animals and tools
Author (year)	`Rybar2023_Simultaneous`
Canonical	—
Importable as	`DS004514`, `Rybar2023_Simultaneous`
Year	20
Authors	Milan Rybář, Riccardo Poli, Ian Daly
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds004514.v1.1.2
Source links	OpenNeuro \| NeMAR \| Source URL

DS004514: eeg, fnirs dataset, 12 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#