DS004022: eeg dataset, 7 subjects#

Multimodal EEG and fNIRS Biosignal Acquisition during Motor Imagery Tasks in Patients with Orthopedic Impairment

Access recordings and metadata through EEGDash.

Citation: Seho Lee, Hee Ra Jung, In-Nea Wang, Min-Kyung Jung, Hakseung Kim, Dong-Joo Kim (2022). Multimodal EEG and fNIRS Biosignal Acquisition during Motor Imagery Tasks in Patients with Orthopedic Impairment. 10.18112/openneuro.ds004022.v1.0.0

Modality: eeg Subjects: 7 Recordings: 21 License: CC0 Source: openneuro Citations: 1.0

Metadata: Complete (100%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import DS004022

dataset = DS004022(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 = DS004022(cache_dir="./data", subject="01")

Advanced query

dataset = DS004022(
    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{ds004022,
  title = {Multimodal EEG and fNIRS Biosignal Acquisition during Motor Imagery Tasks in Patients with Orthopedic Impairment},
  author = {Seho Lee and Hee Ra Jung and In-Nea Wang and Min-Kyung Jung and Hakseung Kim and Dong-Joo Kim},
  doi = {10.18112/openneuro.ds004022.v1.0.0},
  url = {https://doi.org/10.18112/openneuro.ds004022.v1.0.0},
}

About This Dataset#

This dataset consists of raw 18-channel EEG and functional near infrareds(fNIRS) from 7 human paticipants with orthopedic Impairment during motor imagery(MI). The participants performed a series of MI-related trials across three sessions. These sessions comprised 40 trials, of which four different MI tasks were presented in random order (e.g., Reach → Twist → Lift → Reach → Grasp → Grasp → Twist → Reach → Lift → Reach). Each trial began with 3 s of fixation cross. The monitor then displayed a 4 s visual cue, followed by 3 s of letters indicating the ready state with a gray screen to eliminate the afterimage. The participants were then instructed to perform the imaginary movement for 5 s in the given order.

Dataset Information#

Dataset ID	`DS004022`
Title	Multimodal EEG and fNIRS Biosignal Acquisition during Motor Imagery Tasks in Patients with Orthopedic Impairment
Author (year)	`Lee2022`
Canonical	—
Importable as	`DS004022`, `Lee2022`
Year	2022
Authors	Seho Lee, Hee Ra Jung, In-Nea Wang, Min-Kyung Jung, Hakseung Kim, Dong-Joo Kim
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds004022.v1.0.0
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: 7
Recordings: 21
Tasks: 1

Channels & sampling rate

Channels: 18 (19), 16 (2)
Sampling rate (Hz): 500.0
Duration (hours): Not calculated

Electrode Layout#

Electrode layout — EEG · 18 sensors — 18 channels

Dataset Statistics#

Age distribution (n=7, range 48–83 yr)

45557580

Sex distribution

4

3

Female Male Total: 7

Channel counts (ch)

1618

Sampling frequencies: 500.0 Hz (n=21 recordings)

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 DS004022 class to access this dataset programmatically.

class eegdash.dataset.DS004022(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

Multimodal EEG and fNIRS Biosignal Acquisition during Motor Imagery Tasks in Patients with Orthopedic Impairment

Study:: ds004022 (OpenNeuro)
Author (year):: Lee2022
Canonical:: —

Also importable as: DS004022, Lee2022.

Modality: eeg. Subjects: 7; recordings: 21; 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/ds004022 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004022 DOI: https://doi.org/10.18112/openneuro.ds004022.v1.0.0 NEMAR citation count: 1

Examples

>>> from eegdash.dataset import DS004022
>>> dataset = DS004022(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)