moabb.datasets.AlexMI#

class moabb.datasets.AlexMI(subjects=None, sessions=None)[source]#

Bases: BaseDataset

Dataset Snapshot

AlexMI

Motor imagery dataset from the PhD dissertation of A. Barachant. Contains EEG recordings from 8 subjects performing motor imagination tasks (right hand, feet, or rest). Used to validate robust control of an effector via asynchronous EEG-based brain-machine interface.

Motor Imagery, 3 classes (right_hand vs feet vs rest)

AuthorsAlexandre Barachant

🇫🇷 Université de Grenoble, France·2012·alexandre.barachant@gmail.com

Motor Imagery Code: AlexandreMotorImagery 8 subjects 1 session 16 ch 512 Hz 3 classes 3.0 s trials CC BY-SA 4.0

Class Labels: right_hand, feet, rest

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Overview

Alex Motor Imagery dataset.

Motor imagery dataset from the PhD dissertation of A. Barachant

This Dataset contains EEG recordings from 8 subjects, performing 2 task of motor imagination (right hand, feet or rest). Data have been recorded at 512Hz with 16 wet electrodes (Fpz, F7, F3, Fz, F4, F8, T7, C3, Cz, C4, T8, P7, P3, Pz, P4, P8) with a g.tec g.USBamp EEG amplifier.

File are provided in MNE raw file format. A stimulation channel encoding the timing of the motor imagination. The start of a trial is encoded as 1, then the actual start of the motor imagination is encoded with 2 for imagination of a right hand movement, 3 for imagination of both feet movement and 4 with a rest trial.

The duration of each trial is 3 second. There is 20 trial of each class.

Open in Overview tab →

Benchmark Context

WithinSession

Included in 2 MOABB benchmark table(s). Scores are across available pipelines (WithinSession accuracy).

Sample frame: 8 subjects × 1 sessions

MI all classes 16 pipelinesMax 69.79 · Median 60.84 · Mean 55.73 · Std 13.41
MI right hand vs feet 16 pipelinesMax 86.56 · Median 77.11 · Mean 72.08 · Std 11.89

Citation & Impact

Paper DOI10.5281/zenodo.806022
CitationsLoading…
Public APICrossref | OpenAlex
MOABB tables2 (WithinSession)
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30d: 138 · all-time: 1,594
#5 of 151 · Top 4% most viewed
Updated: 2026-03-18 UTC

Stimulus Protocol

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

right_hand

Sensory-eventAgent-action

feet

Sensory-eventAgent-action

rest

Sensory-eventExperimental-stimulusVisual-presentationRest

HED tree view

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 · rest

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

Channel Summary

Total channels16

EEG16 (EEG)

Montage10-05

Sampling512 Hz

Referenceearlobe

Notch / line50 Hz

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

Overview

Alex Motor Imagery dataset.

Motor imagery dataset from the PhD dissertation of A. Barachant [1].

This Dataset contains EEG recordings from 8 subjects, performing 2 task of motor imagination (right hand, feet or rest). Data have been recorded at 512Hz with 16 wet electrodes (Fpz, F7, F3, Fz, F4, F8, T7, C3, Cz, C4, T8, P7, P3, Pz, P4, P8) with a g.tec g.USBamp EEG amplifier.

File are provided in MNE raw file format. A stimulation channel encoding the timing of the motor imagination. The start of a trial is encoded as 1, then the actual start of the motor imagination is encoded with 2 for imagination of a right hand movement, 3 for imagination of both feet movement and 4 with a rest trial.

The duration of each trial is 3 second. There is 20 trial of each class.

References

[1]

Barachant, A., 2012. Commande robuste d’un effecteur par une interface cerveau machine EEG asynchrone (Doctoral dissertation, Université de Grenoble). https://tel.archives-ouvertes.fr/tel-01196752

Code Examples

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

Metadata

Dataset summary

#Subj	8
#Chan	16
#Classes	3
#Trials / class	20
Trials length	3 s
Freq	512 Hz
#Sessions	1
#Runs	1
Total_trials	480

Participants

Population: healthy

Equipment

Amplifier: g.tec g.USBamp
Electrodes: EEG
Montage: standard_1005
Reference: earlobe

Preprocessing

Re-reference: earlobe

Data Access

DOI: 10.5281/zenodo.806022
Data URL: https://zenodo.org/record/806023
Repository: Zenodo

Experimental Protocol

Paradigm: imagery
Feedback: none
Stimulus: visual cue

__init__(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])

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)[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 (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[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.AlexMI`#

Convert a MOABB dataset to BIDS

moabb.datasets.AlexMI#

Examples using moabb.datasets.AlexMI#

Examples using `moabb.datasets.AlexMI`#