DS004152: eeg dataset, 21 subjects#

Drum Trainer

Access recordings and metadata through EEGDash.

Citation: Cameron D. Hassall, Yan Yan, Laurence T. Hunt (2022). Drum Trainer. 10.18112/openneuro.ds004152.v1.1.2

Modality: eeg Subjects: 21 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 DS004152

dataset = DS004152(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 = DS004152(cache_dir="./data", subject="01")

Advanced query

dataset = DS004152(
    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{ds004152,
  title = {Drum Trainer},
  author = {Cameron D. Hassall and Yan Yan and Laurence T. Hunt},
  doi = {10.18112/openneuro.ds004152.v1.1.2},
  url = {https://doi.org/10.18112/openneuro.ds004152.v1.1.2},
}

About This Dataset#

Drum Trainer

Twenty-one participants learned to play two drumming patterns (pattern 1: AABA, pattern 2: AAABAA) at three different tempos (fast: 150 bpm, medium: 100 bpm, slow: 60 bpm). Responses were recorded using the f and j keys on a standard keyboard. Visual feedback in the form of a coloured circle coincided with each button press and indicated whether the response was early, on time, or late. Feedback was determined by comparing the response time, relative to the previous response, to a window around the target duration. The window was adapted trial-by-trial to ensure that roughly half the outcomes were “on time”. Participant 12 should be excluded from event-locked analyses due to bad triggers (trigger cable was partially disconnected). Timing Repeat for 72 trials: fixation dot (until response) -> feedback circle (50 ms) Condition Codes 1: “Fast, pattern 1, left-hand start” 2: “Fast, pattern 1, right-hand start” 3: “Fast, pattern 2, left-hand start” 4: “Fast, pattern 2, right-hand start” 5: “Medium, pattern 1, left-hand start” 6: “Medium, pattern 1, right-hand start” 7: “Medium, pattern 2, left-hand start” 8: “Medium, pattern 2, right-hand start” 9: “Slow, pattern 1, left-hand start” 10: “Slow, pattern 1, right-hand start” 11: “Slow, pattern 2, left-hand start” 12: “Slow, pattern 2, right-hand start” Trigger Modifiers Add 0: Metronome beat (pre-block) Add 20: Ready screen (pre-block) Add 40: First response (can’t be early, on time, or late) Add 60: Early response Add 80: On time response Add 100: Late response Add 120: Red X (wrong key pressed)

Dataset Information#

Dataset ID	`DS004152`
Title	Drum Trainer
Author (year)	`Hassall2022_Drum`
Canonical	—
Importable as	`DS004152`, `Hassall2022_Drum`
Year	2022
Authors	Cameron D. Hassall, Yan Yan, Laurence T. Hunt
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds004152.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: 21
Recordings: 21
Tasks: 1

Channels & sampling rate

Channels: 31
Sampling rate (Hz): 1000.0
Duration (hours): Not calculated

Electrode Layout#

Electrode layout — EEG · 31 sensors — 31 channels

Dataset Statistics#

Age distribution (n=21, range 21–41 yr)

20253040

Sex distribution

16

5

Female Male Total: 21

Channel counts: 31 ch (n=21 recordings)

Sampling frequencies: 1000.0 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 DS004152 class to access this dataset programmatically.

class eegdash.dataset.DS004152(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

Drum Trainer

Study:: ds004152 (OpenNeuro)
Author (year):: Hassall2022_Drum
Canonical:: —

Also importable as: DS004152, Hassall2022_Drum.

Modality: eeg. 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/ds004152 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004152 DOI: https://doi.org/10.18112/openneuro.ds004152.v1.1.2 NEMAR citation count: 1

Examples

>>> from eegdash.dataset import DS004152
>>> dataset = DS004152(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)