DS004514#

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

Access recordings and metadata through EEGDash.

Citation: Milan Rybář, Riccardo Poli, Ian Daly (2023). Simultaneous EEG and fNIRS recordings for semantic decoding of imagined animals and tools. 10.18112/openneuro.ds004514.v1.1.2

Modality: eeg Subjects: 12 Recordings: 161 License: CC0 Source: openneuro

Metadata: Complete (100%)

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},
}

About This Dataset#

Description

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.

EEG

View full README

Description

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.

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).

Dataset Information#

Dataset ID	`DS004514`
Title	Simultaneous EEG and fNIRS recordings for semantic decoding of imagined animals and tools
Year	2023
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

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: 12
Recordings: 161
Tasks: 2

Channels & sampling rate

Channels: 22 (12), 28 (12), 64 (12), 80 (12)
Sampling rate (Hz): 2048.0 (24), 7.8125 (12), 8.928571428571429 (12)
Duration (hours): 0.0

API Reference#

Use the DS004514 class to access this dataset programmatically.

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

Bases: EEGDashDataset

OpenNeuro dataset ds004514. 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

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, 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