DS005429#

Auditory oddball comparison (Optimum-1, Learning-oddball, and the local–global paradigm)

Access recordings and metadata through EEGDash.

Citation: Renate Rutiku, Chiara Fiscone, Marcello Massimini, Simone Sarasso (2024). Auditory oddball comparison (Optimum-1, Learning-oddball, and the local–global paradigm). 10.18112/openneuro.ds005429.v1.0.0

Modality: eeg Subjects: 15 Recordings: 430 License: CC0 Source: openneuro Citations: 0.0

Metadata: Complete (100%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import DS005429

dataset = DS005429(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 = DS005429(cache_dir="./data", subject="01")

Advanced query

dataset = DS005429(
    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{ds005429,
  title = {Auditory oddball comparison (Optimum-1, Learning-oddball, and the local–global paradigm)},
  author = {Renate Rutiku and Chiara Fiscone and Marcello Massimini and Simone Sarasso},
  doi = {10.18112/openneuro.ds005429.v1.0.0},
  url = {https://doi.org/10.18112/openneuro.ds005429.v1.0.0},
}

About This Dataset#

Introduction

This is the raw EEG data used in: Rutiku, R., Fiscone, C., Massimini, M., & Sarasso, S. (2024). Assessing mismatch negativity (MMN) and P3b within‐individual sensitivity — A comparison between the local–global paradigm and two specialized oddball sequences. European Journal of Neuroscience, 59(5), 842-859.

What’s in this dataset

View full README

Introduction

This is the raw EEG data used in: Rutiku, R., Fiscone, C., Massimini, M., & Sarasso, S. (2024). Assessing mismatch negativity (MMN) and P3b within‐individual sensitivity — A comparison between the local–global paradigm and two specialized oddball sequences. European Journal of Neuroscience, 59(5), 842-859.

What’s in this dataset

Each participant (n=15) completed three different auditory oddball sequences: the Optimum-1 for MMN, the learning-oddball for P3b, and the local–global paradigm for the local and global effect. The tasks are formatted as different sessions but they were all recorded consecutively within one EEG experiment (order differed between participants). The local-global sequence was recorded in two separate EEG files (except for participant 5; see below for exception notes). Note that whereas the .vmrk files contain the original triggers for each recording, the _events files contain the correct event samples used in the analysis (in the fieldtrip cfg.trl format). It namely sometimes happened that some triggers were skipped by the recording system and these triggers needed to be interpolated using the event timestamps from the psychtoolbox output that was used to run the stimulation sequence (see below). Note also that the local-global sequence contains triggers for every single sound, but trials should be cut only for the first sound of every quintlet. The _events files already take that into account.

| Subject | Session      | Run   |
| ------- |--------------|-------|
| sub-01  | ses-MMN      |       |
| sub-01  | ses-P3b      |       |
| sub-01  | ses-LGeffect | run-1 |
| sub-01  | ses-LGeffect | run-2 |

Auditory stimulation specs

The stimulation sequence information is provided in the original .mat format in the sourcedata folder. There are two files for each sequence: a file containing the sound definitions (_stimulation_SEQUENCE) and a file containing the timestamps for each sound (_critical_events). The code used to run these sequences is included in the paradigms folder.

Exceptions

Participant 13 was recorded with 5000 Hz EEG sampling rate whereas all other participants were recorded with 2500 Hz EEG sampling rate. Participants 13, 14, and 15 were recorded chronologically first and they have slightly more trials for the oddball sequences. After inspecting their data, it was decided that trial numbers can be reduced for the rest of the participants in order to keep the recording time as short as possible while still having good sensitivity for the effects of interest. Participant 5 has three runs for the local-global task due to a need for an extra break by the participant.

Dataset Information#

Dataset ID	`DS005429`
Title	Auditory oddball comparison (Optimum-1, Learning-oddball, and the local–global paradigm)
Year	2024
Authors	Renate Rutiku, Chiara Fiscone, Marcello Massimini, Simone Sarasso
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds005429.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: 15
Recordings: 430
Tasks: 3

Channels & sampling rate

Channels: 64
Sampling rate (Hz): 2500.0 (114), 5000.0 (8)
Duration (hours): 0.0

API Reference#

Use the DS005429 class to access this dataset programmatically.

class eegdash.dataset.DS005429(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds005429. Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 15; recordings: 61; tasks: 3.

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/ds005429 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005429

Examples

>>> from eegdash.dataset import DS005429
>>> dataset = DS005429(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