Application of transfer learning in EEG decoding based on brain-computer interfaces: a review
The algorithms of electroencephalography (EEG) decoding are mainly based on machine
learning in current research. One of the main assumptions of machine learning is that …
learning in current research. One of the main assumptions of machine learning is that …
A systematic evaluation of euclidean alignment with deep learning for eeg decoding
Objective: Electroencephalography signals are frequently used for various Brain–Computer
interface (BCI) tasks. While deep learning (DL) techniques have shown promising results …
interface (BCI) tasks. While deep learning (DL) techniques have shown promising results …
Evaluating the structure of cognitive tasks with transfer learning
Electroencephalography (EEG) decoding is a challenging task due to the limited availability
of labelled data. While transfer learning is a promising technique to address this challenge, it …
of labelled data. While transfer learning is a promising technique to address this challenge, it …
The Riemannian potato field: a tool for online signal quality index of EEG
Electroencephalographic (EEG) recordings are contaminated by instrumental,
environmental, and biological artifacts, resulting in low signal-to-noise ratio. Artifact …
environmental, and biological artifacts, resulting in low signal-to-noise ratio. Artifact …
Pseudo-online detection and classification for upper-limb movements
Objective. This study analyzed detection (movement vs. non-movement) and classification
(different types of movements) to decode upper-limb movement volitions in a pseudo-online …
(different types of movements) to decode upper-limb movement volitions in a pseudo-online …
Transfer Learning for P300 Brain-Computer Interfaces by Joint Alignment of Feature Vectors
F Altindis, A Banerjee, R Phlypo… - IEEE Journal of …, 2023 - ieeexplore.ieee.org
This article presents a new transfer learning method named group learning, that jointly
aligns multiple domains (many-to-many) and an extension named fast alignment that aligns …
aligns multiple domains (many-to-many) and an extension named fast alignment that aligns …
Riemannian geometry-based transfer learning for reducing training time in c-VEP BCIs
J Ying, Q Wei, X Zhou - Scientific reports, 2022 - nature.com
One of the main problems that a brain-computer interface (BCI) face is that a training stage is
required for acquiring training data to calibrate its classification model just before every use …
required for acquiring training data to calibrate its classification model just before every use …
The largest EEG-based BCI reproducibility study for open science: the MOABB benchmark
Objective. This study conduct an extensive Brain-computer interfaces (BCI) reproducibility
analysis on open electroencephalography datasets, aiming to assess existing solutions and …
analysis on open electroencephalography datasets, aiming to assess existing solutions and …
Minimizing subject-dependent calibration for BCI with Riemannian transfer learning
Calibration is still an important issue for user experience in Brain-Computer Interfaces (BCI).
Common experimental designs often involve a lengthy training period that raises the …
Common experimental designs often involve a lengthy training period that raises the …
Workshops of the eighth international brain–computer interface meeting: BCIs: the next frontier
ABSTRACT The Eighth International Brain–Computer Interface (BCI) Meeting was held June
7–9, 2021 in a virtual format. The conference continued the BCI Meeting series' interactive …
7–9, 2021 in a virtual format. The conference continued the BCI Meeting series' interactive …