moabb.datasets.Pressel2016#

class moabb.datasets.Pressel2016(include_pronounced=False, subjects=None, sessions=None)[source]#

Bases: BaseDataset

[source]

Dataset Snapshot

Pressel2016

Open access database of EEG signals recorded during imagined speech. 15 subjects, 6 channels, 11 classes (5 vowels + 6 directional commands). Presented at SIPAIM 2016. 83 citations.

Imagery, 11 classes

AuthorsGerman A. Pressel Coretto, Ivan E. Gareis, Hugo Leonardo Rufiner

🇦🇷 Universidad Nacional de Entre Rios, AR·2017·germanpressel@gmail.com
Imagery Code: Pressel2016 15 subjects 1 session 6 ch 1024 Hz 11 classes 4.0 s trials

Class Labels: vowel_a, vowel_e, vowel_i, vowel_o, vowel_u, arriba, abajo, adelante, ...

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Overview

Imagined Speech Database - Spanish vowels and commands.

Dataset from Pressel Coretto, Gareis, and Rufiner

Dataset Description

Fifteen Argentinian volunteers (7 female, 8 male, ages 24-28) performed two tasks: imagined speech and pronounced speech of 11 stimuli (5 Spanish vowels: A, E, I, O, U; and 6 directional commands: arriba, abajo, adelante, atras, derecha, izquierda).

EEG was recorded at 1024 Hz from 6 channels (F3, F4, C3, C4, P3, P4) using a Grass 8-18-36 amplifier with a DataTranslation DT9816 ADC. Signals were bandpass filtered at 2-45 Hz.

Each trial is 4 seconds (4096 samples). Data is organized as a matrix where each row is a trial with 6*4096 = 24576 EEG samples concatenated, plus 3 label columns (modality, stimulus, artifact).

By default, only imagined speech trials (modality=1) are loaded. Artifact-flagged trials (artifact=2) are excluded.

:param include_pronounced: If True, include pronounced speech trials as a second session. Default False (imagined speech only). :type include_pronounced: bool

Open in Overview tab →

Citation & Impact

HED Event Tags
HED tags11/11 events annotated

Source: MOABB BIDS HED annotation mapping.

Agent-action
11
Sensory-event
11
vowel_a
Sensory-eventAgent-action
vowel_e
Sensory-eventAgent-action
vowel_i
Sensory-eventAgent-action
vowel_o
Sensory-eventAgent-action
vowel_u
Sensory-eventAgent-action
arriba
Sensory-eventAgent-action
abajo
Sensory-eventAgent-action
adelante
Sensory-eventAgent-action
atras
Sensory-eventAgent-action
derecha
Sensory-eventAgent-action
izquierda
Sensory-eventAgent-action

HED tree view

Tree · vowel_a
├─ Sensory-event
│  ├─ Experimental-stimulus
│  └─ Visual-presentation
└─ Agent-action
   └─ Imagine
      ├─ Speak
      └─ Phoneme
         └─ Label
Tree · vowel_e
├─ Sensory-event
│  ├─ Experimental-stimulus
│  └─ Visual-presentation
└─ Agent-action
   └─ Imagine
      ├─ Speak
      └─ Phoneme
         └─ Label
Tree · vowel_i
├─ Sensory-event
│  ├─ Experimental-stimulus
│  └─ Visual-presentation
└─ Agent-action
   └─ Imagine
      ├─ Speak
      └─ Phoneme
         └─ Label
Tree · vowel_o
├─ Sensory-event
│  ├─ Experimental-stimulus
│  └─ Visual-presentation
└─ Agent-action
   └─ Imagine
      ├─ Speak
      └─ Phoneme
         └─ Label
Tree · vowel_u
├─ Sensory-event
│  ├─ Experimental-stimulus
│  └─ Visual-presentation
└─ Agent-action
   └─ Imagine
      ├─ Speak
      └─ Phoneme
         └─ Label
Tree · arriba
├─ Sensory-event
│  ├─ Experimental-stimulus
│  └─ Visual-presentation
└─ Agent-action
   └─ Imagine
      ├─ Speak
      └─ Word
         └─ Label
Tree · abajo
├─ Sensory-event
│  ├─ Experimental-stimulus
│  └─ Visual-presentation
└─ Agent-action
   └─ Imagine
      ├─ Speak
      └─ Word
         └─ Label
Tree · adelante
├─ Sensory-event
│  ├─ Experimental-stimulus
│  └─ Visual-presentation
└─ Agent-action
   └─ Imagine
      ├─ Speak
      └─ Word
         └─ Label
Tree · atras
├─ Sensory-event
│  ├─ Experimental-stimulus
│  └─ Visual-presentation
└─ Agent-action
   └─ Imagine
      ├─ Speak
      └─ Word
         └─ Label
Tree · derecha
├─ Sensory-event
│  ├─ Experimental-stimulus
│  └─ Visual-presentation
└─ Agent-action
   └─ Imagine
      ├─ Speak
      └─ Word
         └─ Label
Tree · izquierda
├─ Sensory-event
│  ├─ Experimental-stimulus
│  └─ Visual-presentation
└─ Agent-action
   └─ Imagine
      ├─ Speak
      └─ Word
         └─ Label
Channel Summary
Total channels6
EEG6 (EEG)
Montagestandard_1020
Sampling1024 Hz
Filter{'highpass': 2.0, 'lowpass': 45.0}
Notch / line50 Hz

Imagined Speech Database - Spanish vowels and commands.

Dataset from Pressel Coretto, Gareis, and Rufiner [1].

Dataset Description

Fifteen Argentinian volunteers (7 female, 8 male, ages 24-28) performed two tasks: imagined speech and pronounced speech of 11 stimuli (5 Spanish vowels: A, E, I, O, U; and 6 directional commands: arriba, abajo, adelante, atras, derecha, izquierda).

EEG was recorded at 1024 Hz from 6 channels (F3, F4, C3, C4, P3, P4) using a Grass 8-18-36 amplifier with a DataTranslation DT9816 ADC. Signals were bandpass filtered at 2-45 Hz.

Each trial is 4 seconds (4096 samples). Data is organized as a matrix where each row is a trial with 6*4096 = 24576 EEG samples concatenated, plus 3 label columns (modality, stimulus, artifact).

By default, only imagined speech trials (modality=1) are loaded. Artifact-flagged trials (artifact=2) are excluded.

Pressel2016 trial structure (Fig. 1 of the SPIE paper) — Ready interval (2 s) → Stimulus presentation (2 s) → Imagine/Pronounce interval (4 s) → Rest interval (4 s).

Figure 1 of [1] — trial structure (Ready, Stimulus, Imagine/Pronounce, Rest). Reproduced from the author postprint at the sinc(i)/UNL institutional repository.#

param include_pronounced:

If True, include pronounced speech trials as a second session. Default False (imagined speech only).

type include_pronounced:

bool

References

[1] (1,2)

Pressel Coretto, G. A., Gareis, I. E., & Rufiner, H. L. (2017). Open access database of EEG signals recorded during imagined speech. 12th International Symposium on Medical Information Processing and Analysis (SIPAIM 2016), SPIE Proceedings, 10160. https://doi.org/10.1117/12.2255697

from moabb.datasets import Pressel2016
dataset = Pressel2016()
data = dataset.get_data(subjects=[1])
print(data[1])

Dataset summary

#Subj

15

#Chan

6

#Classes

11

#Trials / class

50

Trials length

4 s

Freq

1024 Hz

#Sessions

1

#Runs

1

Total_trials

8670

Participants

  • Population: healthy

Equipment

  • Amplifier: Grass 8-18-36 amplifier + DataTranslation DT9816 ADC

  • Electrodes: EEG

  • Montage: standard_1020

Preprocessing

  • Data state: preprocessed

  • Bandpass filter: 2-45 Hz

  • Steps: Bandpass 2-45 Hz

Data Access

Experimental Protocol

  • Paradigm: imagery

  • Stimulus: visual cue

__init__(include_pronounced=False, subjects=None, sessions=None)[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