moabb.datasets.MIMED2024#

class moabb.datasets.MIMED2024[source]#

Bases: BaseDataset

[source]

Dataset Snapshot

MIMED2024

MIMED: motor imagery and motor execution EEG dataset of six activities recorded from 30 subjects with an Emotiv EPOC X 14-channel headset at 128 Hz.

Imagery, 3 classes (left_hand vs right_hand vs trunk)

AuthorsI Made Agus Wirawan, Dechrit Maneetham, I Gede Mahendra Darmawiguna, Arnon Niyomphol, Pakornkiat Sawetmethikul, Padma Nyoman Crisnapati, Yamin Thwe, Ni Nyoman Mestri Agustini

🇮🇩 Universitas Pendidikan Ganesha, ID·2024
Imagery Code: MIMED2024 30 subjects 2 sessions 14 ch 128 Hz 3 classes 4.0 s trials CC BY 4.0

Class Labels: left_hand, right_hand, trunk

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Overview

Motor Imagery MIMED dataset from Wirawan et al. 2024

Dataset description

MIMED (Motor Imagery and Motor Execution Dataset): 30 healthy students recorded with a 14-channel Emotiv EPOC X headset at 128 Hz while executing and imagining six activities (raise/lower each hand, stand/sit). Only the imagery recordings are loaded: three scenario files per subject, each with four blocks over two days. Each block starts with a 3 s (384-sample) baseline; the event marks imagery onset, and each block contributes one 4 s window. The .mat files carry no per-repetition label, so each scenario folder is one class: left_hand (1), right_hand (2) and trunk (3, stand/sit). The two days are separate sessions with six runs each. Signals are stored in Emotiv raw microvolts (DC offset ~4200 uV) and are rescaled to volts on load.

The Data in Brief article reports 30 participants (16 men, 14 women) recorded at the Data Science Laboratories, Engineering and Vocational Faculty, Universitas Pendidikan Ganesha (Bali, Indonesia), on two different days with two scenarios per day, and four imagery repetitions per activity (six activities, "twenty-four trials"); the loader exposes four blocks per scenario folder, i.e. 12 windows per subject, because the .mat files carry no per-repetition activity label. The Emotiv CMS/DRL reference is a device property and is not stated in the article.

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Citation & Impact

Stimulus Protocol
../_images/MIMED2024.svg

4s task window per trial · 3-class imagery paradigm · 6 runs/session across 2 sessions

HED Event Tags
HED tags3/3 events annotated

Source: MOABB BIDS HED annotation mapping.

Sensory-event
3
Agent-action
2
Label
1
left_hand
Sensory-eventAgent-action
right_hand
Sensory-eventAgent-action
trunk
Sensory-eventLabel

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 · trunk
├─ Sensory-event
└─ Label
Channel Summary
Total channels14
EEG14 (saline felt (Ag/AgCl))
Montagestandard_1020
Sampling128 Hz
ReferenceCMS/DRL (P3/P4)
Notch / line50 Hz

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

Motor Imagery MIMED dataset from Wirawan et al. 2024 [1].

Dataset description

MIMED (Motor Imagery and Motor Execution Dataset): 30 healthy students recorded with a 14-channel Emotiv EPOC X headset at 128 Hz while executing and imagining six activities (raise/lower each hand, stand/sit). Only the imagery recordings are loaded: three scenario files per subject, each with four blocks over two days. Each block starts with a 3 s (384-sample) baseline; the event marks imagery onset, and each block contributes one 4 s window. The .mat files carry no per-repetition label, so each scenario folder is one class: left_hand (1), right_hand (2) and trunk (3, stand/sit). The two days are separate sessions with six runs each. Signals are stored in Emotiv raw microvolts (DC offset ~4200 uV) and are rescaled to volts on load.

The Data in Brief article reports 30 participants (16 men, 14 women) recorded at the Data Science Laboratories, Engineering and Vocational Faculty, Universitas Pendidikan Ganesha (Bali, Indonesia), on two different days with two scenarios per day, and four imagery repetitions per activity (six activities, “twenty-four trials”); the loader exposes four blocks per scenario folder, i.e. 12 windows per subject, because the .mat files carry no per-repetition activity label. The Emotiv CMS/DRL reference is a device property and is not stated in the article.

References

[1]

Wirawan, I. M. A., et al. (2024). Acquisition and processing of Motor Imagery and Motor Execution Dataset (MIMED) for six movement activities. Data in Brief, 56, 110833. DOI: https://doi.org/10.1016/j.dib.2024.110833

from moabb.datasets import MIMED2024
dataset = MIMED2024()
data = dataset.get_data(subjects=[1])
print(data[1])

Dataset summary

#Subj

30

#Chan

14

#Classes

3

#Trials / class

4

Trials length

4 s

Freq

128 Hz

#Sessions

2

#Runs

6

Total_trials

360

Participants

  • Population: healthy

Equipment

  • Amplifier: Emotiv EPOC X

  • Electrodes: saline felt (Ag/AgCl)

  • Montage: standard_1020

  • Reference: CMS/DRL (P3/P4)

Data Access

Experimental Protocol

  • Paradigm: imagery

  • Stimulus: video

Notes

An earlier revision derived a 6-class up/down labelling from the acquisition order (even repetition -> raise, odd -> lower). That mapping is not carried by the .mat files and was dropped; only the folder-level 3-class labelling is data-borne.

Added in version 1.8.

__init__()[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 one .mat path per imagery scenario for subject.

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/.