moabb.datasets.PhysionetMI#

class moabb.datasets.PhysionetMI(imagined=True, executed=False, subjects=None, sessions=None, *, return_all_modalities=False, **kwargs)[source]#

Bases: BaseDataset

[source]

Dataset Snapshot

PhysionetMI

Motor Imagery, 5 classes (left_hand vs right_hand vs feet vs hands vs rest)

AuthorsGerwin Schalk, Dennis J. McFarland, Thilo Hinterberger, Niels Birbaumer, Jonathan R. Wolpaw

🇺🇸 Wadsworth Center, New York State Department of Health, USA·2004·schalk@wadsworth.org
Motor Imagery Code: PhysionetMotorImagery 109 subjects 1 session 64 ch 160 Hz 5 classes 3.0 s trials ODC-By 1.0

Class Labels: left_hand, right_hand, feet, hands, rest

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Overview

Physionet Motor Imagery dataset.

Physionet MI dataset: https://physionet.org/pn4/eegmmidb/

This data set consists of over 1500 one- and two-minute EEG recordings, obtained from 109 volunteers

Subjects performed different motor/imagery tasks while 64-channel EEG were recorded using the BCI2000 system (http://www.bci2000.org) Each subject performed 14 experimental runs: two one-minute baseline runs (one with eyes open, one with eyes closed), and three two-minute runs of each of the four following tasks:

1. A target appears on either the left or the right side of the screen. The subject opens and closes the corresponding fist until the target disappears. Then the subject relaxes.

2. A target appears on either the left or the right side of the screen. The subject imagines opening and closing the corresponding fist until the target disappears. Then the subject relaxes.

3. A target appears on either the top or the bottom of the screen. The subject opens and closes either both fists (if the target is on top) or both feet (if the target is on the bottom) until the target disappears. Then the subject relaxes.

4. A target appears on either the top or the bottom of the screen. The subject imagines opening and closing either both fists (if the target is on top) or both feet (if the target is on the bottom) until the target disappears. Then the subject relaxes.

:param imagined: if True, return runs corresponding to motor imagination. :type imagined: bool (default True) :param executed: if True, return runs corresponding to motor execution. :type executed: bool (default False)

Open in Overview tab →

Benchmark Context

WithinSession

Included in 3 MOABB benchmark table(s). Scores are across available pipelines (WithinSession accuracy).

Sample frame: 109 subjects × 1 sessions

  • MI left vs right 19 pipelinesMax 68.46 · Median 63.55 · Mean 62.15 · Std 5.46
  • MI all classes 16 pipelinesMax 59.93 · Median 46.17 · Mean 43.51 · Std 12.69
  • MI right hand vs feet 16 pipelinesMax 94.27 · Median 85.19 · Mean 79.69 · Std 14.42

Citation & Impact

  • Paper DOI10.1109/TBME.2004.827072
  • CitationsLoading…
  • Public APICrossref | OpenAlex
  • MOABB tables3 (WithinSession)
  • Page Views
    30d: 192 · all-time: 2,204
    #2 of 151 · Top 2% most viewed
    Updated: 2026-03-21 UTC
Stimulus Protocol
../_images/PhysionetMI.svg

3s task window per trial · 5-class motor imagery paradigm · 6 runs/session across 1 sessions

HED Event Tags
HED tags5/5 events annotated

Source: MOABB BIDS HED annotation mapping.

Sensory-event
5
Agent-action
4
Experimental-stimulus
1
Rest
1
Visual-presentation
1
left_hand
Sensory-eventAgent-action
right_hand
Sensory-eventAgent-action
feet
Sensory-eventAgent-action
hands
Sensory-eventAgent-action
rest
Sensory-eventExperimental-stimulusVisual-presentationRest

HED tree view

Tree · left_hand
├─ Sensory-event
│  ├─ Experimental-stimulus
│  └─ Visual-presentation
└─ Agent-action
   └─ Imagine
      ├─ Move
      └─ Left
         └─ Hand
Tree · right_hand
├─ Sensory-event
│  ├─ Experimental-stimulus
│  └─ Visual-presentation
└─ Agent-action
   └─ Imagine
      ├─ Move
      └─ Right
         └─ Hand
Tree · feet
├─ Sensory-event
│  ├─ Experimental-stimulus
│  └─ Visual-presentation
└─ Agent-action
   └─ Imagine
      ├─ Move
      └─ Foot
Tree · hands
├─ Sensory-event
│  ├─ Experimental-stimulus
│  └─ Visual-presentation
└─ Agent-action
   └─ Imagine
      ├─ Move
      └─ Hand
Tree · rest
├─ Sensory-event
├─ Experimental-stimulus
├─ Visual-presentation
└─ Rest
Channel Summary
Total channels64
EEG64 (EEG)
Montagestandard_1020
Sampling160 Hz
Referencemastoid
Notch / line60 Hz

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

Physionet Motor Imagery dataset.

Physionet MI dataset: https://physionet.org/pn4/eegmmidb/

This data set consists of over 1500 one- and two-minute EEG recordings, obtained from 109 volunteers [2].

Subjects performed different motor/imagery tasks while 64-channel EEG were recorded using the BCI2000 system (http://www.bci2000.org) [1]. Each subject performed 14 experimental runs: two one-minute baseline runs (one with eyes open, one with eyes closed), and three two-minute runs of each of the four following tasks:

  1. A target appears on either the left or the right side of the screen. The subject opens and closes the corresponding fist until the target disappears. Then the subject relaxes.

  2. A target appears on either the left or the right side of the screen. The subject imagines opening and closing the corresponding fist until the target disappears. Then the subject relaxes.

  3. A target appears on either the top or the bottom of the screen. The subject opens and closes either both fists (if the target is on top) or both feet (if the target is on the bottom) until the target disappears. Then the subject relaxes.

  4. A target appears on either the top or the bottom of the screen. The subject imagines opening and closing either both fists (if the target is on top) or both feet (if the target is on the bottom) until the target disappears. Then the subject relaxes.

Note

Subject 88 was recorded at 128 Hz instead of 160 Hz like all other subjects. Loading subject 88 together with other subjects will cause errors in any paradigm due to incompatible sampling rates. To avoid this, exclude subject 88 when loading the full dataset, e.g. PhysionetMI(subjects=[s for s in range(1, 110) if s != 88]).

param imagined:

if True, return runs corresponding to motor imagination.

type imagined:

bool (default True)

param executed:

if True, return runs corresponding to motor execution.

type executed:

bool (default False)

References

[1]

Schalk, G., McFarland, D.J., Hinterberger, T., Birbaumer, N. and Wolpaw, J.R., 2004. BCI2000: a general-purpose brain-computer interface (BCI) system. IEEE Transactions on biomedical engineering, 51(6), pp.1034-1043.

[2]

Goldberger, A.L., Amaral, L.A., Glass, L., Hausdorff, J.M., Ivanov, P.C., Mark, R.G., Mietus, J.E., Moody, G.B., Peng, C.K., Stanley, H.E. and PhysioBank, P., PhysioNet: components of a new research resource for complex physiologic signals Circulation 2000 Volume 101 Issue 23 pp. E215–E220.

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

Dataset summary

#Subj

109

#Chan

64

#Classes

4

#Trials / class

23

Trials length

3 s

Freq

160 Hz

#Sessions

1

#Runs

1

Total_trials

69760

Participants

  • Population: healthy

Equipment

  • Amplifier: Brain Products

  • Electrodes: EEG

  • Montage: standard_1020

  • Reference: mastoid

Preprocessing

  • Data state: raw EEG stored with all event markers for offline reconstruction

  • Steps: calibration (linear transformation to microvolts), spatial filtering, temporal filtering

  • Re-reference: common average

Data Access

  • DOI: 10.1109/TBME.2004.827072

  • Repository: Physionet

Experimental Protocol

  • Paradigm: imagery

  • Feedback: visual

  • Stimulus: cue-based

__init__(imagined=True, executed=False, 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