moabb.datasets.Sosulski2019#

class moabb.datasets.Sosulski2019(use_soas_as_sessions=True, load_soa_60=False, reject_non_iid=False, interval=None, subjects=None, sessions=None, *, return_all_modalities=False, **kwargs)[source]#

Bases: BaseDataset

[source]

Dataset Snapshot

Sosulski2019

Auditory oddball ERP dataset from 13 healthy subjects. Two sinusoidal tones (target 1000 Hz, non-target 500 Hz) presented at various stimulus onset asynchronies (SOAs, 60-600 ms). 31-channel EEG recorded at 1000 Hz with BrainProducts BrainAmp DC. Raw BrainVision format data.

P300 / ERP, 2 classes (Target vs NonTarget)

AuthorsJan Sosulski, David Hübner, Aaron Klein, Michael Tangermann

🇩🇪 University of Freiburg, DE·2021·jan.sosulski@blbt.uni-freiburg.de
P300 / ERP Code: Sosulski2019 13 subjects 80 sessions 31 ch 1000 Hz 2 classes 1.2 s trials CC BY-SA 4.0

Class Labels: Target, NonTarget

Read Paper Compare Similar Report Issue

Overview

P300 dataset from initial spot study.

Dataset, study on spatial transfer between SOAs, actual paradigm / online optimization

Dataset description This dataset contains multiple small trials of an auditory oddball paradigm. The paradigm presented two different sinusoidal tones. A low-pitched (500 Hz, 40 ms duration) non-target tone and a high-pitched (1000 Hz, 40 ms duration) target tone. Subjects were instructed to attend to the high-pitched target tones and ignore the low-pitched tones.

One trial (= one file) consisted of 90 tones, 15 targets and 75 non-targets. The order was pseudo-randomized in a way that at least two non-target tones occur between two target tones. Additionally, if you split the 90 tones of one trial into consecutive sets of six tones, there will always be exactly one target and five non-target tones in each set.

In the first part of the experiment (run 1), each subject performed 50-70 trials with various different stimulus onset asynchronies (SOAs) -- i.e. the time between the onset of successive tones -- for each trial. In the second part (run 2), 4-5 SOAs were played, with blocks of 5 trials having the same SOA. All SOAs were in the range of 60 ms to 600 ms. Regardless of the experiment part, after a set of five trials, subjects were given the opportunity to take a short break to e.g. drink etc.

Finally, before and after each run, resting data was recorded. One minute with eyes open and one minute with eyes closed, i.e. in total four minutes of resting data are available for each subject.

Data was recorded using a BrainAmp DC (BrainVision) amplifier and a 31 passive electrode EasyCap. The cap was placed according to the extended 10-20 electrode layout. The reference electrode was placed on the nose. Before recording, the cap was prepared such that impedances on all electrodes were around 20 kOhm. The EEG signal was recorded at 1000 Hz.

The data contains 31 scalp channels, one EOG channel and five miscellaneous non-EEG signal channels. However, only scalp EEG and the EOG channel is available in all subjects. The markers in the marker file indicate the onset of target tones (21) and non-target tones (1).

Open in Overview tab →

Benchmark Context

WithinSession

Included in 1 MOABB benchmark table(s). Scores are across available pipelines (WithinSession accuracy).

Sample frame: 13 subjects × 80 sessions

  • ERP/P300 all classes 5 pipelinesMax 87.28 · Median 70.63 · Mean 75.93 · Std 9.89

Citation & Impact

Stimulus Protocol
../_images/Sosulski2019.svg

1.2s task window per trial · 2-class p300 / erp paradigm · 1 runs/session across 80 sessions

HED Event Tags
HED tags2/2 events annotated

Source: MOABB BIDS HED annotation mapping.

Experimental-stimulus
2
Sensory-event
2
Visual-presentation
2
Non-target
1
Target
1
Target
Sensory-eventExperimental-stimulusVisual-presentationTarget
NonTarget
Sensory-eventExperimental-stimulusVisual-presentationNon-target

HED tree view

Tree · Target
├─ Sensory-event
├─ Experimental-stimulus
├─ Visual-presentation
└─ Target
Tree · NonTarget
├─ Sensory-event
├─ Experimental-stimulus
├─ Visual-presentation
└─ Non-target
Channel Summary
Total channels31
EEG31 (passive Ag/AgCl)
MISC5
EOG1
Montagestandard_1020
Sampling1000 Hz
Referencenose
Notch / line50 Hz

This diagram is automatically generated from MOABB metadata. Please consult the original publication to confirm the experimental protocol details.

P300 dataset from initial spot study.

Dataset [1], study on spatial transfer between SOAs [2], actual paradigm / online optimization [3].

Dataset description This dataset contains multiple small trials of an auditory oddball paradigm. The paradigm presented two different sinusoidal tones. A low-pitched (500 Hz, 40 ms duration) non-target tone and a high-pitched (1000 Hz, 40 ms duration) target tone. Subjects were instructed to attend to the high-pitched target tones and ignore the low-pitched tones.

One trial (= one file) consisted of 90 tones, 15 targets and 75 non-targets. The order was pseudo-randomized in a way that at least two non-target tones occur between two target tones. Additionally, if you split the 90 tones of one trial into consecutive sets of six tones, there will always be exactly one target and five non-target tones in each set.

In the first part of the experiment (run 1), each subject performed 50-70 trials with various different stimulus onset asynchronies (SOAs) – i.e. the time between the onset of successive tones – for each trial. In the second part (run 2), 4-5 SOAs were played, with blocks of 5 trials having the same SOA. All SOAs were in the range of 60 ms to 600 ms. Regardless of the experiment part, after a set of five trials, subjects were given the opportunity to take a short break to e.g. drink etc.

Finally, before and after each run, resting data was recorded. One minute with eyes open and one minute with eyes closed, i.e. in total four minutes of resting data are available for each subject.

Data was recorded using a BrainAmp DC (BrainVision) amplifier and a 31 passive electrode EasyCap. The cap was placed according to the extended 10-20 electrode layout. The reference electrode was placed on the nose. Before recording, the cap was prepared such that impedances on all electrodes were around 20 kOhm. The EEG signal was recorded at 1000 Hz.

The data contains 31 scalp channels, one EOG channel and five miscellaneous non-EEG signal channels. However, only scalp EEG and the EOG channel is available in all subjects. The markers in the marker file indicate the onset of target tones (21) and non-target tones (1).

Caution

Note that this wrapper currently only loads the second part of the experiment and uses pseudo-sessions to achieve the functionality to handle different conditions in MOABB. As a result, the statistical testing features of MOABB cannot be used for this dataset.

References

[1]

Sosulski, J., Tangermann, M.: Electroencephalogram signals recorded from 13 healthy subjects during an auditory oddball paradigm under different stimulus onset asynchrony conditions. Dataset. DOI: 10.6094/UNIFR/154576

[2]

Sosulski, J., Tangermann, M.: Spatial filters for auditory evoked potentials transfer between different experimental conditions. Graz BCI Conference. 2019.

[3]

Sosulski, J., Hübner, D., Klein, A., Tangermann, M.: Online Optimization of Stimulation Speed in an Auditory Brain-Computer Interface under Time Constraints. arXiv preprint. 2021.

from moabb.datasets import Sosulski2019
dataset = Sosulski2019()
data = dataset.get_data(subjects=[1])
print(data[1])

Dataset summary

#Subj

13

#Chan

31

#Trials / class

7500 NT / 1500 T

Trials length

1.2 s

Freq

1000 Hz

#Sessions

1

Participants

  • Population: healthy

  • Age: 22.7 (range: 20-26) years

Equipment

  • Amplifier: BrainProducts BrainAmp DC

  • Electrodes: passive Ag/AgCl

  • Montage: standard_1020

  • Reference: nose

Data Access

Experimental Protocol

  • Paradigm: p300

  • Stimulus: oddball

Notes

Added in version 0.4.5.

__init__(use_soas_as_sessions=True, load_soa_60=False, reject_non_iid=False, interval=None, subjects=None, sessions=None, *, return_all_modalities=False, **kwargs)[source]#

Initialize function for the BaseDataset.

property all_subjects#

Full list of subjects available in this dataset (unfiltered).

convert_to_bids(path=None, subjects=None, overwrite=False, format='EDF', verbose=None, generate_figures=False)[source]#

Convert the dataset to BIDS format.

Saves the raw EEG data in a BIDS-compliant directory structure. Unlike the caching mechanism (see CacheConfig), the files produced here do not contain a processing-pipeline hash (desc-<hash>) in their names, making the output a clean, shareable BIDS dataset.

Parameters:

  • path (str | Path | None) – Directory under which the BIDS dataset will be written. If None the default MNE data directory is used (same default as the rest of MOABB).

  • subjects (list of int | None) – Subject numbers to convert. If None, all subjects in subject_list are converted.

  • overwrite (bool) – If True, existing BIDS files for a subject are removed before saving. Default is False.

  • format (str) – The file format for the raw EEG data. Supported values are "EDF" (default), "BrainVision", and "EEGLAB".

  • verbose (str | None) – Verbosity level forwarded to MNE/MNE-BIDS.

  • generate_figures (bool) – If True, generate interactive neural signature HTML figures in {bids_root}/derivatives/neural_signatures/. Requires plotly (pip install moabb[interactive]). Default is False.

Returns:

bids_root – Path to the root of the written BIDS dataset.

Return type:

pathlib.Path

Examples

>>> from moabb.datasets import AlexMI
>>> dataset = AlexMI()
>>> bids_root = dataset.convert_to_bids(path='/tmp/bids', subjects=[1])

Notes

Use CacheConfig to configure caching for get_data(). Use moabb.datasets.bids_interface.get_bids_root to get the BIDS root path.

Added in version 1.5.

data_path(subject, path=None, force_update=False, update_path=None, verbose=None)[source]#

Get path to local copy of a subject data.

Parameters:

  • subject (int) – Number of subject to use

  • path (None | str) – Location of where to look for the data storing location. If None, the environment variable or config parameter MNE_DATASETS_(dataset)_PATH is used. If it doesn’t exist, the “~/mne_data” directory is used. If the dataset is not found under the given path, the data will be automatically downloaded to the specified folder.

  • force_update (bool) – Force update of the dataset even if a local copy exists.

  • update_path (bool | None Deprecated) – If True, set the MNE_DATASETS_(dataset)_PATH in mne-python config to the given path. If None, the user is prompted.

  • verbose (bool, str, int, or None) – If not None, override default verbose level (see mne.verbose()).

Returns:

path – Local path to the given data file. This path is contained inside a list of length one, for compatibility.

Return type:

list of str

download(subject_list=None, path=None, force_update=False, update_path=None, accept=False, verbose=None)[source]#

Download all data from the dataset.

This function is only useful to download all the dataset at once.

Parameters:

  • subject_list (list of int | None) – List of subjects id to download, if None all subjects are downloaded.

  • path (None | str) – Location of where to look for the data storing location. If None, the environment variable or config parameter MNE_DATASETS_(dataset)_PATH is used. If it doesn’t exist, the “~/mne_data” directory is used. If the dataset is not found under the given path, the data will be automatically downloaded to the specified folder.

  • force_update (bool) – Force update of the dataset even if a local copy exists.

  • update_path (bool | None) – If True, set the MNE_DATASETS_(dataset)_PATH in mne-python config to the given path. If None, the user is prompted.

  • accept (bool) – Accept licence term to download the data, if any. Default: False

  • verbose (bool, str, int, or None) – If not None, override default verbose level (see mne.verbose()).

get_additional_metadata(subject: str, session: str, run: str)[source]#

Load additional metadata for a specific subject, session, and run.

This method is intended to be overridden by subclasses to provide additional metadata specific to the dataset. The metadata is typically loaded from an events.tsv file or similar data source.

Parameters:

  • subject (str) – The identifier for the subject.

  • session (str) – The identifier for the session.

  • run (str) – The identifier for the run.

Returns:

A DataFrame containing the additional metadata if available, otherwise None.

Return type:

None | pandas.DataFrame

get_block_repetition(paradigm, subjects, block_list, repetition_list)[source]#

Select data for all provided subjects, blocks and repetitions.

subject -> session -> run -> block -> repetition

See also

get_data

Parameters:

  • subjects (List of int) – List of subject number

  • block_list (List of int) – List of block number

  • repetition_list (List of int) – List of repetition number inside a block

Returns:

data – dict containing the raw data

Return type:

Dict

get_data(subjects=None, cache_config=None, process_pipeline=None)[source]#

Return the data corresponding to a list of subjects.

The returned data is a dictionary with the following structure:

data = {'subject_id' :
            {'session_id':
                {'run_id': run}
            }
        }

subjects are on top, then we have sessions, then runs. A sessions is a recording done in a single day, without removing the EEG cap. A session is constitued of at least one run. A run is a single contiguous recording. Some dataset break session in multiple runs.

Processing steps can optionally be applied to the data using the *_pipeline arguments. These pipelines are applied in the following order: raw_pipeline -> epochs_pipeline -> array_pipeline. If a *_pipeline argument is None, the step will be skipped. Therefore, the array_pipeline may either receive a mne.io.Raw or a mne.Epochs object as input depending on whether epochs_pipeline is None or not.

Parameters:

  • subjects (List of int) – List of subject number

  • cache_config (dict | CacheConfig) – Configuration for caching of datasets. See CacheConfig for details.

  • process_pipeline (sklearn.pipeline.Pipeline | None) – Optional processing pipeline to apply to the data. To generate an adequate pipeline, we recommend using moabb.make_process_pipelines(). This pipeline will receive mne.io.BaseRaw objects. The steps names of this pipeline should be elements of StepType. According to their name, the steps should either return a mne.io.BaseRaw, a mne.Epochs, or a numpy.ndarray. This pipeline must be “fixed” because it will not be trained, i.e. no call to fit will be made.

Returns:

data – dict containing the raw data

Return type:

Dict

property metadata[source]#

Return structured metadata for this dataset.

Returns the DatasetMetadata object from the centralized catalog, or None if metadata is not available for this dataset.

Returns:

The metadata object containing acquisition parameters, participant demographics, experiment details, and documentation. Returns None if no metadata is registered for this dataset.

Return type:

DatasetMetadata | None

Examples

>>> from moabb.datasets import BNCI2014_001
>>> dataset = BNCI2014_001()
>>> dataset.metadata.participants.n_subjects
9
>>> dataset.metadata.acquisition.sampling_rate
250.0

Examples using moabb.datasets.Sosulski2019#

Dataset bubble plot

Dataset bubble plot