moabb.datasets.Lee2019_ERP#

class moabb.datasets.Lee2019_ERP(train_run=True, test_run=None, resting_state=False, sessions=None, subjects=None, *, return_all_modalities=False, **kwargs)[source]#

Bases: Lee2019

[source]

Dataset Snapshot

Lee2019_ERP

EEG dataset and OpenBMI toolbox for three BCI paradigms: an investigation into BCI illiteracy

P300 / ERP, 2 classes (Target vs NonTarget)

AuthorsMin-Ho Lee, O-Yeon Kwon, Yong-Jeong Kim, Hong-Kyung Kim, Young-Eun Lee, John Williamson, Siamac Fazli, Seong-Whan Lee

🇰🇷 Korea University, KR·2019·sw.lee@korea.ac.kr

P300 / ERP Code: Lee2019-ERP 54 subjects 2 sessions 62 ch 1000 Hz 2 classes 1.0 s trials GPL 3.0

Class Labels: Target, NonTarget

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Overview

BMI/OpenBMI dataset for P300.

Dataset from Lee et al 2019

Dataset Description

EEG signals were recorded with a sampling rate of 1,000 Hz and collected with 62 Ag/AgCl electrodes. The EEG amplifier used in the experiment was a BrainAmp (Brain Products; Munich, Germany). The channels were nasion-referenced and grounded to electrode AFz. Additionally, an EMG electrode recorded from each flexor digitorum profundus muscle with the olecranon used as reference. The EEG/EMG channel configuration and indexing numbers are described in Fig. 1. The impedances of the EEG electrodes were maintained below 10 k during the entire experiment.

ERP paradigm The interface layout of the speller followed the typical design of a row-column speller. The six rows and six columns were configured with 36 symbols (A to Z, 1 to 9, and _). Each symbol was presented equally spaced. To enhance the signal quality, two additional settings were incorporated into the original row-column speller design, namely, random-set presentation and face stimuli. These additional settings help to elicit stronger ERP responses by minimizing adjacency distraction errors and by presenting a familiar face image. The stimulus-time interval was set to 80 ms, and the inter-stimulus interval (ISI) to 135 ms. A single iteration of stimulus presentation in all rows and columns was considered a sequence. Therefore, one sequence consisted of 12 stimulus flashes. A maximum of five sequences (i.e., 60 flashes) was allotted without prolonged inter-sequence intervals for each target character. After the end of five sequences, 4.5 s were given to the user for identifying, locating, and gazing at the next target character. The participant was instructed to attend to the target symbol by counting the number of times each target character had been flashed. In the training session, subjects were asked to copy-spell a given sentence, "NEURAL NETWORKS AND DEEP LEARNING" (33 characters including spaces) by gazing at the target character on the screen. The training session was performed in the offline condition, and no feedback was provided to the subject during the EEG recording. In the test session, subjects were instructed to copy-spell "PATTERN RECOGNITION MACHINE LEARNING" (36 characters including spaces), and the real-time EEG data were analyzed based on the classifier that was calculated from the training session data. The selected character from the subject’s current EEG data was displayed in the top left area of the screen at the end of the presentation (i.e., after five sequences). Per participant, the collected EEG data for the ERP experiment consisted of 1,980 and 2,160 trials (samples) for training and test phase, respectively.

:param train_run: if True, return runs corresponding to the training/offline phase (see paper). :type train_run: bool (default True) :param test_run: if True, return runs corresponding to the test/online phase (see paper). Beware that test_run for MI and SSVEP do not have labels associated with trials: these runs could not be used in classification tasks. :type test_run: bool (default: False for MI and SSVEP paradigms, True for ERP) :param resting_state: if True, return runs corresponding to the resting phases before and after recordings (see paper). :type resting_state: bool (default False) :param sessions: the list of the sessions to load (2 available). :type sessions: list of int (default [1,2])

Open in Overview tab →

Benchmark Context

WithinSession

Included in 1 MOABB benchmark table(s). Scores are across available pipelines (WithinSession accuracy).

Sample frame: 54 subjects × 2 sessions

ERP/P300 all classes 5 pipelinesMax 98.41 · Median 96.45 · Mean 89.89 · Std 10.85

Citation & Impact

Paper DOI10.1093/gigascience/giz002
CitationsLoading…
Public APICrossref | OpenAlex
Data DOI10.5524/100542
MOABB tables1 (WithinSession)
Page Views
30d: 65 · all-time: 616
#14 of 151 · Top 10% most viewed
Updated: 2026-03-18 UTC

Stimulus Protocol

1s task window per trial · 2-class p300 / erp paradigm · 2 runs/session across 2 sessions

HED Event Tags

HED tags2/2 events annotated

Source: MOABB BIDS HED annotation mapping.

Experimental-stimulus

2

Sensory-event

2

Visual-presentation

2

Non-target

1

Target

1

Target

Sensory-eventExperimental-stimulusVisual-presentationTarget

NonTarget

Sensory-eventExperimental-stimulusVisual-presentationNon-target

HED tree view

Tree · Target

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

Tree · NonTarget

├─ Sensory-event
├─ Experimental-stimulus
├─ Visual-presentation
└─ Non-target

Channel Summary

Total channels62

EEG62 (Ag/AgCl)

EMG4

Montage10-05

Sampling1000 Hz

Referencenasion

Notch / line60 Hz

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

Overview

BMI/OpenBMI dataset for P300.

Dataset from Lee et al 2019 [1].

Dataset Description

EEG signals were recorded with a sampling rate of 1,000 Hz and collected with 62 Ag/AgCl electrodes. The EEG amplifier used in the experiment was a BrainAmp (Brain Products; Munich, Germany). The channels were nasion-referenced and grounded to electrode AFz. Additionally, an EMG electrode recorded from each flexor digitorum profundus muscle with the olecranon used as reference. The EEG/EMG channel configuration and indexing numbers are described in Fig. 1. The impedances of the EEG electrodes were maintained below 10 k during the entire experiment.

ERP paradigm The interface layout of the speller followed the typical design of a row-column speller. The six rows and six columns were configured with 36 symbols (A to Z, 1 to 9, and _). Each symbol was presented equally spaced. To enhance the signal quality, two additional settings were incorporated into the original row-column speller design, namely, random-set presentation and face stimuli. These additional settings help to elicit stronger ERP responses by minimizing adjacency distraction errors and by presenting a familiar face image. The stimulus-time interval was set to 80 ms, and the inter-stimulus interval (ISI) to 135 ms. A single iteration of stimulus presentation in all rows and columns was considered a sequence. Therefore, one sequence consisted of 12 stimulus flashes. A maximum of five sequences (i.e., 60 flashes) was allotted without prolonged inter-sequence intervals for each target character. After the end of five sequences, 4.5 s were given to the user for identifying, locating, and gazing at the next target character. The participant was instructed to attend to the target symbol by counting the number of times each target character had been flashed. In the training session, subjects were asked to copy-spell a given sentence, “NEURAL NETWORKS AND DEEP LEARNING” (33 characters including spaces) by gazing at the target character on the screen. The training session was performed in the offline condition, and no feedback was provided to the subject during the EEG recording. In the test session, subjects were instructed to copy-spell “PATTERN RECOGNITION MACHINE LEARNING” (36 characters including spaces), and the real-time EEG data were analyzed based on the classifier that was calculated from the training session data. The selected character from the subject’s current EEG data was displayed in the top left area of the screen at the end of the presentation (i.e., after five sequences). Per participant, the collected EEG data for the ERP experiment consisted of 1,980 and 2,160 trials (samples) for training and test phase, respectively.

param train_run:: if True, return runs corresponding to the training/offline phase (see paper).
type train_run:: bool (default True)
param test_run:: if True, return runs corresponding to the test/online phase (see paper). Beware that test_run for MI and SSVEP do not have labels associated with trials: these runs could not be used in classification tasks.
type test_run:: bool (default: False for MI and SSVEP paradigms, True for ERP)
param resting_state:: if True, return runs corresponding to the resting phases before and after recordings (see paper).
type resting_state:: bool (default False)
param sessions:: the list of the sessions to load (2 available).
type sessions:: list of int (default [1,2])

References

[1]

Lee, M. H., Kwon, O. Y., Kim, Y. J., Kim, H. K., Lee, Y. E., Williamson, J., … Lee, S. W. (2019). EEG dataset and OpenBMI toolbox for three BCI paradigms: An investigation into BCI illiteracy. GigaScience, 8(5), 1–16. https://doi.org/10.1093/gigascience/giz002

Code Examples

from moabb.datasets import Lee2019_ERP
dataset = Lee2019_ERP()
data = dataset.get_data(subjects=[1])
print(data[1])

Metadata

Dataset summary

#Subj	54
#Chan	62
#Trials / class	6900 NT / 1380 T
Trials length	1 s
Freq	1000 Hz
#Sessions	2

Participants

Population: healthy
Age: 29.5 (range: 24-35) years
Handedness: right
BCI experience: mixed

Equipment

Amplifier: BrainAmp
Electrodes: Ag/AgCl
Montage: standard_1005
Reference: nasion

Preprocessing

Data state: raw

Data Access

DOI: 10.1093/gigascience/giz002
Repository: GigaDB

Experimental Protocol

Paradigm: p300
Task type: copy_spelling
Feedback: visual
Stimulus: rc_speller

__init__(train_run=True, test_run=None, resting_state=False, sessions=None, subjects=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])

Examples using `moabb.datasets.Lee2019_ERP`#

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moabb.datasets.Lee2019_ERP#

Examples using moabb.datasets.Lee2019_ERP#

Examples using `moabb.datasets.Lee2019_ERP`#