DS004264#

Steer the Ship

Access recordings and metadata through EEGDash.

Citation: Cameron D. Hassall, Yan Yan, Laurence T. Hunt (2022). Steer the Ship. 10.18112/openneuro.ds004264.v1.1.0

Modality: eeg Subjects: 21 Recordings: 194 License: CC0 Source: openneuro Citations: 0.0

Metadata: Complete (100%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import DS004264

dataset = DS004264(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 = DS004264(cache_dir="./data", subject="01")

Advanced query

dataset = DS004264(
    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{ds004264,
  title = {Steer the Ship},
  author = {Cameron D. Hassall and Yan Yan and Laurence T. Hunt},
  doi = {10.18112/openneuro.ds004264.v1.1.0},
  url = {https://doi.org/10.18112/openneuro.ds004264.v1.1.0},
}

About This Dataset#

Steer the Ship

Twenty-one participants learned to control the trajectory of a ship, represented by centrally presented rotating arrow. Prior to each round participants were cued about the degree of controller and environmental noise (“wind”) they would experience. During the round, participants pressed the ‘f’ and ‘j’ keys to apply angular force in either a clockwise or counterclockwise direction. The goal of the task was to keep the ship closely oriented towards a target. Points were accumulated depending on the mean distance to target. The ship would crash if it strayed too far from the target (and the round would end). Each round lasted up to 1 minute. The underlying physics were based on the pole-and-cart problem (i.e., unstable).

There were four noise conditions: 1: No noise 2: Environmental noise only (ship occasionally moved on its own) 3: Controller noise only (amount of force varied) 4: Environmental and controller noise

Participant 12 should be excluded from event-locked analyses due to bad triggers (trigger cable was partially disconnected).

Also note that the RT for the first button press in each round is not recorded (but is recorded in the participantActions column).

Trigger Modifiers (added to condition numbers) Add 0: Condition cue Add 10: Start of round Add 20: Left button press Add 30: Right button press Add 40: Left button press (computer) Add 50: Right button press (computer) Add 60: Crash Add 70: Success (reached 1 minute of play) Add 80: Points displayed

Dataset Information#

Dataset ID	`DS004264`
Title	Steer the Ship
Year	2022
Authors	Cameron D. Hassall, Yan Yan, Laurence T. Hunt
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds004264.v1.1.0
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: 21
Recordings: 194
Tasks: 1

Channels & sampling rate

Channels: 31
Sampling rate (Hz): 1000.0
Duration (hours): 0.0

API Reference#

Use the DS004264 class to access this dataset programmatically.

class eegdash.dataset.DS004264(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds004264. Modality: eeg; Experiment type: Learning; Subject type: Healthy. Subjects: 21; 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/ds004264 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004264

Examples

>>> from eegdash.dataset import DS004264
>>> dataset = DS004264(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