moabb.datasets.BNCI2024_001#

class moabb.datasets.BNCI2024_001(subjects=None, sessions=None, *, return_all_modalities=False)[source]#

Bases: BNCIBaseDataset

[source]

Dataset Snapshot

BNCI2024_001

Classification of handwritten letters from EEG through continuous kinematic decoding

Motor Imagery, 10 classes

AuthorsMarkus R. Crell, Gernot R. MΓΌller-Putz

πŸ‡¦πŸ‡Ήβ€‚Graz University of Technology, AustriaΒ·2024Β·gernot.mueller@tugraz.at
Motor Imagery Code: BNCI2024-001 20 subjects 1 session 60 ch 500 Hz 10 classes 8.5 s trials CC BY 4.0

Class Labels: letter_a, letter_d, letter_e, letter_f, letter_j, letter_n, letter_o, letter_s, ...

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Overview

BNCI 2024-001 Handwritten Character Classification dataset.

Dataset from

Dataset Description

This dataset contains EEG data from 20 healthy subjects performing handwritten character (letter) writing tasks. Participants wrote 10 different letters (a, d, e, f, j, n, o, s, t, v) while EEG was recorded. The study investigates the classification of handwritten characters from non-invasive EEG through continuous kinematic decoding.

Participants

  • - 20 healthy subjects
  • Location: Institute of Neural Engineering, Graz University of Technology, Austria

Recording Details

  • - Equipment: BrainVision EEG system with 60 EEG + 4 EOG channels
  • Channels: 60 EEG electrodes + 4 EOG electrodes = 64 total
  • Electrode montage: Extended 10-20 system
  • Sampling rate: 500 Hz

Experimental Procedure

  • - 10 letter classes: a, d, e, f, j, n, o, s, t, v
  • Participants wrote letters inside a box while fixating on the screen
  • No visual feedback of the writing was provided during the task
  • 2 experimental rounds per subject, each containing ~32 trials per letter
  • Additional motion capture data was recorded (pen position)

Event Codes

The events correspond to the 10 different letters written by participants:

  • - letter_a (1): Letter 'a'
  • letter_d (2): Letter 'd'
  • letter_e (3): Letter 'e'
  • letter_f (4): Letter 'f'
  • letter_j (5): Letter 'j'
  • letter_n (6): Letter 'n'
  • letter_o (7): Letter 'o'
  • letter_s (8): Letter 's'
  • letter_t (9): Letter 't'
  • letter_v (10): Letter 'v'
Open in Overview tab β†’

Citation & Impact

Stimulus Protocol
../_images/BNCI2024_001.svg

8.5s task window per trial Β· 10-class motor imagery paradigm Β· 2 runs/session across 1 sessions

HED Event Tags
HED tags10/10 events annotated

Source: MOABB BIDS HED annotation mapping.

Agent-action
10
Sensory-event
10
letter_a
Sensory-eventAgent-action
letter_d
Sensory-eventAgent-action
letter_e
Sensory-eventAgent-action
letter_f
Sensory-eventAgent-action
letter_j
Sensory-eventAgent-action
letter_n
Sensory-eventAgent-action
letter_o
Sensory-eventAgent-action
letter_s
Sensory-eventAgent-action
letter_t
Sensory-eventAgent-action
letter_v
Sensory-eventAgent-action

HED tree view

Tree Β· letter_a
β”œβ”€ Sensory-event
β”‚  β”œβ”€ Experimental-stimulus
β”‚  └─ Visual-presentation
└─ Agent-action
   └─ Write
      β”œβ”€ Hand
      └─ Label
Tree Β· letter_d
β”œβ”€ Sensory-event
β”‚  β”œβ”€ Experimental-stimulus
β”‚  └─ Visual-presentation
└─ Agent-action
   └─ Write
      β”œβ”€ Hand
      └─ Label
Tree Β· letter_e
β”œβ”€ Sensory-event
β”‚  β”œβ”€ Experimental-stimulus
β”‚  └─ Visual-presentation
└─ Agent-action
   └─ Write
      β”œβ”€ Hand
      └─ Label
Tree Β· letter_f
β”œβ”€ Sensory-event
β”‚  β”œβ”€ Experimental-stimulus
β”‚  └─ Visual-presentation
└─ Agent-action
   └─ Write
      β”œβ”€ Hand
      └─ Label
Tree Β· letter_j
β”œβ”€ Sensory-event
β”‚  β”œβ”€ Experimental-stimulus
β”‚  └─ Visual-presentation
└─ Agent-action
   └─ Write
      β”œβ”€ Hand
      └─ Label
Tree Β· letter_n
β”œβ”€ Sensory-event
β”‚  β”œβ”€ Experimental-stimulus
β”‚  └─ Visual-presentation
└─ Agent-action
   └─ Write
      β”œβ”€ Hand
      └─ Label
Tree Β· letter_o
β”œβ”€ Sensory-event
β”‚  β”œβ”€ Experimental-stimulus
β”‚  └─ Visual-presentation
└─ Agent-action
   └─ Write
      β”œβ”€ Hand
      └─ Label
Tree Β· letter_s
β”œβ”€ Sensory-event
β”‚  β”œβ”€ Experimental-stimulus
β”‚  └─ Visual-presentation
└─ Agent-action
   └─ Write
      β”œβ”€ Hand
      └─ Label
Tree Β· letter_t
β”œβ”€ Sensory-event
β”‚  β”œβ”€ Experimental-stimulus
β”‚  └─ Visual-presentation
└─ Agent-action
   └─ Write
      β”œβ”€ Hand
      └─ Label
Tree Β· letter_v
β”œβ”€ Sensory-event
β”‚  β”œβ”€ Experimental-stimulus
β”‚  └─ Visual-presentation
└─ Agent-action
   └─ Write
      β”œβ”€ Hand
      └─ Label
Channel Summary
Total channels60
EEG60 (active electrodes)
EOG4
Montageeogl1 eogl2 eogl3 eogr1 af7 af3 afz af4 af8 f7 f5 f3 f1 fz f2 f4 f6 f8 ft7 fc5 fc3 fc1 fcz fc2 fc4 fc6 ft8 t7 c5 c3 c1 cz c2 c4 c6 t8 tp7 cp5 cp3 cp1 cpz cp2 cp4 cp6 tp8 p7 p5 p3 p1 pz p2 p4 p6 p8 ppo1h ppo2h po7 po3 poz po4 po8 o1 oz o2
Sampling500 Hz
Referenceright mastoid
Filter50 Hz notch
Notch / line50 Hz

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

BNCI 2024-001 Handwritten Character Classification dataset.

Dataset from [1].

Dataset Description

This dataset contains EEG data from 20 healthy subjects performing handwritten character (letter) writing tasks. Participants wrote 10 different letters (a, d, e, f, j, n, o, s, t, v) while EEG was recorded. The study investigates the classification of handwritten characters from non-invasive EEG through continuous kinematic decoding.

Participants

  • 20 healthy subjects

  • Location: Institute of Neural Engineering, Graz University of Technology, Austria

Recording Details

  • Equipment: BrainVision EEG system with 60 EEG + 4 EOG channels

  • Channels: 60 EEG electrodes + 4 EOG electrodes = 64 total

  • Electrode montage: Extended 10-20 system

  • Sampling rate: 500 Hz

Experimental Procedure

  • 10 letter classes: a, d, e, f, j, n, o, s, t, v

  • Participants wrote letters inside a box while fixating on the screen

  • No visual feedback of the writing was provided during the task

  • 2 experimental rounds per subject, each containing ~32 trials per letter

  • Additional motion capture data was recorded (pen position)

Event Codes

The events correspond to the 10 different letters written by participants:

  • letter_a (1): Letter β€˜a’

  • letter_d (2): Letter β€˜d’

  • letter_e (3): Letter β€˜e’

  • letter_f (4): Letter β€˜f’

  • letter_j (5): Letter β€˜j’

  • letter_n (6): Letter β€˜n’

  • letter_o (7): Letter β€˜o’

  • letter_s (8): Letter β€˜s’

  • letter_t (9): Letter β€˜t’

  • letter_v (10): Letter β€˜v’

References

[1]

Crell, M. R., & Muller-Putz, G. R. (2024). Handwritten character classification from EEG through continuous kinematic decoding. Computers in Biology and Medicine, 182, 109132. https://doi.org/10.1016/j.compbiomed.2024.109132

from moabb.datasets import BNCI2024_001
dataset = BNCI2024_001()
data = dataset.get_data(subjects=[1])
print(data[1])

Dataset summary

#Subj

20

#Chan

64

#Classes

10

#Trials / class

varies

Trials length

3 s

Freq

512 Hz

#Sessions

1

#Runs

1

Total_trials

varies

Participants

  • Population: healthy

  • Age: 27.5 years

  • Handedness: {β€˜right’: 22}

  • BCI experience: not specified

Equipment

  • Amplifier: BrainVision

  • Electrodes: active electrodes

  • Montage: eogl1 eogl2 eogl3 eogr1 af7 af3 afz af4 af8 f7 f5 f3 f1 fz f2 f4 f6 f8 ft7 fc5 fc3 fc1 fcz fc2 fc4 fc6 ft8 t7 c5 c3 c1 cz c2 c4 c6 t8 tp7 cp5 cp3 cp1 cpz cp2 cp4 cp6 tp8 p7 p5 p3 p1 pz p2 p4 p6 p8 ppo1h ppo2h po7 po3 poz po4 po8 o1 oz o2

  • Reference: right mastoid

Preprocessing

  • Data state: raw

  • Bandpass filter: 0.3-70 Hz

  • Steps: notch filtering, bandpass filtering, bad channel interpolation, EOG artifact correction (SGEYESUB), ICA for artifact removal, re-referencing to CAR, bad segment rejection, lowpass filtering, downsampling, epoching

  • Re-reference: car

  • Notes: Two datasets created: dataset 1 (0.3-3 Hz, 10 Hz sampling) and dataset 2 (0.3-40 Hz, 128 Hz sampling). Bad segments rejected if exceeding Β±120 ΞΌV or kurtosis/probability > 7 SD from mean.

Data Access

Experimental Protocol

  • Paradigm: imagery

  • Task type: handwriting

  • Feedback: Training included visual feedback showing finger position; main paradigm had no feedback during writing (only fixation cross)

  • Stimulus: letter cue

Notes

Added in version 1.3.0.

This dataset is notable for exploring non-invasive EEG-based handwritten character classification, which could enable communication for individuals with limited movement capacity. The study demonstrated that handwritten characters can be classified from non-invasive EEG and that decoding movement kinematics prior to classification improves performance.

__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])

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)[source]#

Return paths to data files for a single 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.

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 β€œ~/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[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 | pd.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

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 β€œfixed” 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