moabb.datasets.Ofner2017

class moabb.datasets.Ofner2017(imagined=True, executed=True, subjects=None, sessions=None, *, return_all_modalities=False, **kwargs)

Bases: BaseDataset

[source]

Dataset Snapshot

Ofner2017

Motor Imagery, 7 classes

AuthorsPatrick Ofner, Andreas Schwarz, Joana Pereira, Gernot R. Müller-Putz

🇦🇹 Graz University of Technology, AT·2017·gernot.mueller@tugraz.at

Motor Imagery Code: Ofner2017 15 subjects 2 sessions 61 ch 512 Hz 7 classes 3.0 s trials CC BY 4.0

Class Labels: right_elbow_flexion, right_elbow_extension, right_supination, right_pronation, right_hand_close, right_hand_open, rest

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Overview

Motor Imagery ataset from Ofner et al 2017.

Upper limb Motor imagery dataset from the paper

Dataset description

We recruited 15 healthy subjects aged between 22 and 40 years with a mean age of 27 years (standard deviation 5 years). Nine subjects were female, and all the subjects except s1 were right-handed.

We measured each subject in two sessions on two different days, which were not separated by more than one week. In the first session the subjects performed ME, and MI in the second session. The subjects performed six movement types which were the same in both sessions and comprised of elbow flexion/extension, forearm supination/pronation and hand open/close; all with the right upper limb. All movements started at a neutral position: the hand half open, the lower arm extended to 120 degree and in a neutral rotation, i.e. thumb on the inner side. Additionally to the movement classes, a rest class was recorded in which subjects were instructed to avoid any movement and to stay in the starting position. In the ME session, we instructed subjects to execute sustained movements. In the MI session, we asked subjects to perform kinesthetic MI of the movements done in the ME session (subjects performed one ME run immediately before the MI session to support kinesthetic MI).

The paradigm was trial-based and cues were displayed on a computer screen in front of the subjects, Fig 2 shows the sequence of the paradigm. At second 0, a beep sounded and a cross popped up on the computer screen (subjects were instructed to fixate their gaze on the cross). Afterwards, at second 2, a cue was presented on the computer screen, indicating the required task (one out of six movements or rest) to the subjects. At the end of the trial, subjects moved back to the starting position. In every session, we recorded 10 runs with 42 trials per run. We presented 6 movement classes and a rest class and recorded 60 trials per class in a session.

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Citation & Impact

• Paper DOI10.1371/journal.pone.0182578
• CitationsLoading…
• Public APICrossref | OpenAlex
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  Updated: 2026-03-12 UTC

Stimulus Protocol

3s task window per trial · 7-class motor imagery paradigm · 10 runs/session across 2 sessions

HED Event Tags

HED tags7/7 events annotated

Source: MOABB BIDS HED annotation mapping.

Sensory-event

7

Agent-action

6

Experimental-stimulus

1

Rest

1

Visual-presentation

1

right_elbow_flexion

Sensory-eventAgent-action

right_elbow_extension

Sensory-eventAgent-action

right_supination

Sensory-eventAgent-action

right_pronation

Sensory-eventAgent-action

right_hand_close

Sensory-eventAgent-action

right_hand_open

Sensory-eventAgent-action

rest

Sensory-eventExperimental-stimulusVisual-presentationRest

HED tree view

Tree · right_elbow_flexion

├─ Sensory-event
│  ├─ Experimental-stimulus
│  └─ Visual-presentation
└─ Agent-action
   └─ Imagine
      ├─ Flex
      └─ Right
         └─ Elbow

Tree · right_elbow_extension

├─ Sensory-event
│  ├─ Experimental-stimulus
│  └─ Visual-presentation
└─ Agent-action
   └─ Imagine
      ├─ Stretch
      └─ Right
         └─ Elbow

Tree · right_supination

├─ Sensory-event
│  ├─ Experimental-stimulus
│  └─ Visual-presentation
└─ Agent-action
   └─ Imagine
      ├─ Turn
      ├─ Right
      │  └─ Forearm
      └─ Label

Tree · right_pronation

├─ Sensory-event
│  ├─ Experimental-stimulus
│  └─ Visual-presentation
└─ Agent-action
   └─ Imagine
      ├─ Turn
      ├─ Right
      │  └─ Forearm
      └─ Label

Tree · right_hand_close

├─ Sensory-event
│  ├─ Experimental-stimulus
│  └─ Visual-presentation
└─ Agent-action
   └─ Imagine
      ├─ Close
      └─ Right
         └─ Hand

Tree · right_hand_open

├─ Sensory-event
│  ├─ Experimental-stimulus
│  └─ Visual-presentation
└─ Agent-action
   └─ Imagine
      ├─ Open
      └─ Right
         └─ Hand

Tree · rest

├─ Sensory-event
├─ Experimental-stimulus
├─ Visual-presentation
└─ Rest

Channel Summary

Total channels61

EEG61 (active)

MISC32

EOG3

Montage10-05

Sampling512 Hz

Referenceright mastoid

Filter0.01-200 Hz bandpass (8th order Chebyshev), 50 Hz notch

Notch / line50 Hz

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

Overview

Motor Imagery ataset from Ofner et al 2017.

Upper limb Motor imagery dataset from the paper [1].

Dataset description

We recruited 15 healthy subjects aged between 22 and 40 years with a mean age of 27 years (standard deviation 5 years). Nine subjects were female, and all the subjects except s1 were right-handed.

We measured each subject in two sessions on two different days, which were not separated by more than one week. In the first session the subjects performed ME, and MI in the second session. The subjects performed six movement types which were the same in both sessions and comprised of elbow flexion/extension, forearm supination/pronation and hand open/close; all with the right upper limb. All movements started at a neutral position: the hand half open, the lower arm extended to 120 degree and in a neutral rotation, i.e. thumb on the inner side. Additionally to the movement classes, a rest class was recorded in which subjects were instructed to avoid any movement and to stay in the starting position. In the ME session, we instructed subjects to execute sustained movements. In the MI session, we asked subjects to perform kinesthetic MI of the movements done in the ME session (subjects performed one ME run immediately before the MI session to support kinesthetic MI).

The paradigm was trial-based and cues were displayed on a computer screen in front of the subjects, Fig 2 shows the sequence of the paradigm. At second 0, a beep sounded and a cross popped up on the computer screen (subjects were instructed to fixate their gaze on the cross). Afterwards, at second 2, a cue was presented on the computer screen, indicating the required task (one out of six movements or rest) to the subjects. At the end of the trial, subjects moved back to the starting position. In every session, we recorded 10 runs with 42 trials per run. We presented 6 movement classes and a rest class and recorded 60 trials per class in a session.

References

[1]

Ofner, P., Schwarz, A., Pereira, J. and Müller-Putz, G.R., 2017. Upper limb movements can be decoded from the time-domain of low-frequency EEG. PloS one, 12(8), p.e0182578. https://doi.org/10.1371/journal.pone.0182578

Code Examples

from moabb.datasets import Ofner2017
dataset = Ofner2017()
data = dataset.get_data(subjects=[1])
print(data[1])

Metadata

Dataset summary

#Subj	15
#Chan	61
#Classes	7
#Trials / class	60
Trials length	3 s
Freq	512 Hz
#Sessions	1
#Runs	10
Total_trials	63000

Participants

• Population: healthy

• Age: 27 (range: 22-40) years

• Handedness: {‘right’: 14, ‘left’: 1}

Equipment

• Amplifier: g.tec medical engineering GmbH

• Electrodes: active

• Montage: standard_1005

• Reference: right mastoid

Data Access

• DOI: 10.1371/journal.pone.0182578

• Data URL: https://bnci-horizon-2020.eu/database/data-sets

• Repository: BNCI Horizon 2020

Experimental Protocol

• Paradigm: imagery

• Feedback: none

• Stimulus: visual cue

__init__(imagined=True, executed=True, subjects=None, sessions=None, *, return_all_modalities=False, **kwargs)

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)

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.

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])

See also

CacheConfig

Cache configuration for get_data().

moabb.datasets.bids_interface.get_bids_root

Return the BIDS root path.

Notes

Added in version 1.5.

data_path(subject, path=None, force_update=False, update_path=None, verbose=None, session=None)

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)

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) → None | DataFrame

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 | pd.DataFrame

get_block_repetition(paradigm, subjects, block_list, repetition_list)

Select data for all provided subjects, blocks and repetitions.

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

See also

BaseDataset.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)

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 (Pipeline | None) – Optional processing pipeline to apply to the data. To generate an adequate pipeline, we recommend using moabb.utils.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: DatasetMetadata | None

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