moabb.datasets.Tavakolan2017#

class moabb.datasets.Tavakolan2017(subjects=None, sessions=None, *, return_all_modalities=False)[source]#

Bases: BaseDataset

[source]

Dataset Snapshot

Tavakolan2017

Imagery, 3 classes (rest vs right_hand vs right_elbow_flexion)

AuthorsMojgan Tavakolan, Zack Frehlick, Xinyi Yong, Carlo Menon

🇨🇦 Simon Fraser University, CA·2017
Imagery Code: Tavakolan2017 12 subjects 4 sessions 32 ch 1000 Hz 3 classes 3.0 s trials CC0 1.0

Class Labels: rest, right_hand, right_elbow_flexion

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Overview

Motor imagery dataset for three imaginary states of the same upper extremity.

Dataset from

This dataset contains 32-channel EEG recordings from 12 healthy subjects performing motor imagery of the right upper extremity. Subjects imagined three tasks: rest, grasping (opening/closing fingers to grab an object), and elbow flexion/extension (moving the forearm up and down).

EEG was recorded at 1000 Hz using a 32-channel EGI Geodesic Sensor Net (GES 400 series amplifier) with Cz as the online reference. Each subject completed 4 sessions on separate days, with 20 trials per class per session (80 trials total per session, 4 classes).

Each trial consisted of a 3 s visual cue (during which the subject performed the imagery) followed by a 4-6 s rest interval. The imagery interval [0, 3] s after cue onset is used for analysis.

The data was originally deposited on the Dryad Digital Repository and has been re-hosted on Zenodo for direct programmatic access.

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Citation & Impact

Stimulus Protocol
../_images/Tavakolan2017.svg

3s task window per trial · 3-class imagery paradigm · 1 runs/session across 4 sessions

HED Event Tags
HED tags3/3 events annotated

Source: MOABB BIDS HED annotation mapping.

Sensory-event
3
Agent-action
2
Experimental-stimulus
1
Rest
1
Visual-presentation
1
rest
Sensory-eventExperimental-stimulusVisual-presentationRest
right_hand
Sensory-eventAgent-action
right_elbow_flexion
Sensory-eventAgent-action

HED tree view

Tree · rest
├─ Sensory-event
├─ Experimental-stimulus
├─ Visual-presentation
└─ Rest
Tree · right_hand
├─ Sensory-event
│  ├─ Experimental-stimulus
│  └─ Visual-presentation
└─ Agent-action
   └─ Imagine
      ├─ Move
      └─ Right
         └─ Hand
Tree · right_elbow_flexion
├─ Sensory-event
│  ├─ Experimental-stimulus
│  └─ Visual-presentation
└─ Agent-action
   └─ Imagine
      ├─ Flex
      └─ Right
         └─ Elbow
Channel Summary
Total channels32
EEG32 (Ag/AgCl sponge)
MontageGSN-HydroCel-32
Sampling1000 Hz
ReferenceCz
Filter{'bandpass': [0.1, 100]}
Notch / line60 Hz

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

Motor imagery dataset for three imaginary states of the same upper extremity.

Dataset from [1].

This dataset contains 32-channel EEG recordings from 12 healthy subjects performing motor imagery of the right upper extremity. Subjects imagined three tasks: rest, grasping (opening/closing fingers to grab an object), and elbow flexion/extension (moving the forearm up and down).

EEG was recorded at 1000 Hz using a 32-channel EGI Geodesic Sensor Net (GES 400 series amplifier) with Cz as the online reference. Each subject completed 4 sessions on separate days, with 20 trials per class per session (80 trials total per session, 4 classes).

Each trial consisted of a 3 s visual cue (during which the subject performed the imagery) followed by a 4-6 s rest interval. The imagery interval [0, 3] s after cue onset is used for analysis.

The data was originally deposited on the Dryad Digital Repository [2] and has been re-hosted on Zenodo for direct programmatic access.

Note

Reading BCI2000 .DAT files requires the BCI2kReader package:

pip install BCI2kReader

References

[1]

M. Tavakolan, Z. Frehlick, X. Yong, and C. Menon, “Classifying three imaginary states of the same upper extremity using time-domain features,” PLoS ONE, vol. 12, no. 3, e0174161, 2017. DOI: 10.1371/journal.pone.0174161

[2]

M. Tavakolan, Z. Frehlick, X. Yong, and C. Menon, “Data from: Classifying three imaginary states of the same upper extremity using time-domain features,” Dryad, 2017. DOI: 10.5061/dryad.6qs86

from moabb.datasets import Tavakolan2017
dataset = Tavakolan2017()
data = dataset.get_data(subjects=[1])
print(data[1])

Dataset summary

#Subj

12

#Chan

32

#Classes

3

#Trials / class

20

Trials length

3 s

Freq

1000 Hz

#Sessions

4

#Runs

1

Total_trials

2880

Participants

  • Population: healthy

Equipment

  • Amplifier: EGI Geodesic Net Amps 400 series

  • Electrodes: Ag/AgCl sponge

  • Montage: GSN-HydroCel-32

  • Reference: Cz

Preprocessing

  • Data state: continuous

Data Access

Experimental Protocol

  • Paradigm: imagery

  • Feedback: none

  • Stimulus: visual cue

Notes

The original channel labels follow the EGI HydroCel Geodesic Sensor Net naming convention (E1-E32 plus Cz reference). The GSN-HydroCel-32 montage from MNE is applied.

The raw BCI2000 files contain 280 source channels; only the first 32 are EEG. Channels are scaled from raw ADC units to volts using the gain from the BCI2000 header (0.0238419 µV per count).

The BCI2000 files actually contain four stimulus classes (Rest, Wrist, Elbow, Reach-Hold the Glass) with StimulusCodes 1-4. Following the paper’s analysis of three classes, only codes 1-3 are mapped to events by default.

__init__(subjects=None, sessions=None, *, return_all_modalities=False)[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]#

Return local path to the subject directory containing .DAT files.

Downloads the per-subject ZIP from Zenodo if not already present, then extracts the nested session ZIPs to obtain the BCI2000 .DAT files.

Parameters:

  • subject (int) – Subject number (1-12).

  • path (str | None) – Custom download location.

  • force_update (bool) – Force re-download.

  • update_path (None) – Unused, kept for API compatibility.

  • verbose (bool | None) – Verbosity level.

Returns:

data_dir – Path to the dataset root directory.

Return type:

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.

When the dataset declares a nemar_id and the download provider is not "upstream", the files come from NEMAR’s sourcedata/ – the original pre-BIDS distribution, byte-identical to what the upstream host serves. On any NEMAR failure this falls back to the dataset’s own downloader with a warning (unless the provider is pinned to "nemar"). See sourcedata_path() and moabb.set_download_provider().

Parameters:

  • subject_list (list of int | None) – List of subjects id to download, if None all subjects are downloaded. On the NEMAR path each subject is resolved through the deposit’s sourcedata_provenance.json; deposits enriched before that manifest recorded subjects fetch the whole tree.

  • 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. Not used on the NEMAR path.

  • accept (bool) – Accept licence term to download the data, if any. Default: False. Only relevant to the dataset’s own downloader; NEMAR mirrors are already public.

  • 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, n_jobs=1)[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.

  • n_jobs (int) – Number of jobs to run in parallel over subjects (passed to joblib.Parallel). Default 1 (sequential). Per-subject processing (reading, filtering, resampling, epoching) is independent, so this gives a near-linear speedup for datasets with many subjects.

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

Get the dataset’s original pre-BIDS distribution from NEMAR.

Where data_path() fetches the original files from the upstream host, this fetches the copy NEMAR mirrors under sourcedata/. The files keep their upstream names, so the two are interchangeable in content – but NEMAR stays reachable when the upstream host is slow, rate-limited, behind a bot gate, or retired.

Parameters:

  • subject (int | str | None) – Restrict the download to one subject, resolved through the deposit’s sourcedata_provenance.json. Deposits enriched before that manifest recorded subjects fall back to the whole tree with a warning. When None the whole tree is fetched.

  • path (None | str) – Base path where MOABB stores datasets.

  • force_update (bool) – Re-fetch even when a local copy is present.

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

Returns:

Local path to the sourcedata directory.

Return type:

str

Raises:

  • ValueError – If the dataset declares no nemar_id.

  • moabb.datasets.download.NemarDownloadError – If the download fails or the deposit publishes no sourcedata/.