DS003190#

Assesment of the visual stimuli properties in P300 paradigm

Access recordings and metadata through EEGDash.

Citation: Omar Mendoza-Montoya, Javier M. Antelis (2020). Assesment of the visual stimuli properties in P300 paradigm. 10.18112/openneuro.ds003190.v1.0.1

Modality: eeg Subjects: 19 Recordings: 1685 License: CC0 Source: openneuro Citations: 4.0

Metadata: Complete (100%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import DS003190

dataset = DS003190(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 = DS003190(cache_dir="./data", subject="01")

Advanced query

dataset = DS003190(
    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{ds003190,
  title = {Assesment of the visual stimuli properties in P300 paradigm},
  author = {Omar Mendoza-Montoya and Javier M. Antelis},
  doi = {10.18112/openneuro.ds003190.v1.0.1},
  url = {https://doi.org/10.18112/openneuro.ds003190.v1.0.1},
}

About This Dataset#

Dataset description:

The database consists of a total of 382 electroencephalographic files from 19 participants. All recordings were collected on channels Fz, Cz, P3, Pz,P4, PO7, PO8 and Oz, according to the 10-20 EEG electrode placement standard, grounded to AFz channel and referenced to right mastoid (M2). - Each participant (S1-S19) performed 3 experimental sessions (Session01-Session03) and in each session there are 7 data files. - The filenames for these data files are ’Training 4’, ’Training 5 - SF’, ’Training 5 - CF’, ’Training 6’, ’Training 7’, ’Training 8’, and ’Training 9’. - The number accompanying the filename indicates the number of stimuli, whereas letters SF and CF for data files with 5 stimuli indicate the type of flash, SF for Standard-Flash of the stimulus and CF for superimposing a yellow smiling Cartoon Face. - Note that filenames for data-files with 4, 6, 7, 8, and 9 stimuli do not have a letter and were recorded with the type of flash that provided the greater classification accuracy when using 5 stimuli. - Each data file contains the data stream in a 2D matrix where rows correspond to channels and columns correspond to time samples with sampling frequency of 256Hz. - There are 10 rows, 1 to 8 for each EEG electrode (in descending order Fz, Cz, P3, Pz, P4, PO7, PO8 and Oz), 9 for time stamps, and 10 for a marker that encode information about the execution of theexperiment.

The marker encodes this information as follows: - (i)marker numbers 101, 200, 201, 202 and 203, indicate the beginning and end of the five phases in a block - (ii)marker numbers 1, 2, 3, 4, 5, 6, 7, 8 and 9, indicate the symbol that is activated on the screen - (iii)each phase of the experiment block is identified with a marker - (iv)the phases of one block of the experiment are: Fixation, Target Presentation, Preparation, Stimulation and Rest - (iv)in particular the Stimulation phase has a start marker and an end marker

Dataset Information#

Dataset ID	`DS003190`
Title	Assesment of the visual stimuli properties in P300 paradigm
Year	2020
Authors	Omar Mendoza-Montoya, Javier M. Antelis
License	CC0
Citation / DOI	10.18112/openneuro.ds003190.v1.0.1
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: 19
Recordings: 1685
Tasks: 1

Channels & sampling rate

Channels: 9 (764), 10 (4)
Sampling rate (Hz): 256.0
Duration (hours): 0.0

API Reference#

Use the DS003190 class to access this dataset programmatically.

class eegdash.dataset.DS003190(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds003190. Modality: eeg; Experiment type: Perception. Subjects: 19; recordings: 384; 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/ds003190 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003190

Examples

>>> from eegdash.dataset import DS003190
>>> dataset = DS003190(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