MDTL: a novel and model-agnostic transfer learning strategy for cross-subject motor imagery BCI
In recent years, deep neural network-based transfer learning (TL) has shown outstanding
performance in EEG-based motor imagery (MI) brain-computer interface (BCI). However …
performance in EEG-based motor imagery (MI) brain-computer interface (BCI). However …
Adaptive transfer learning for EEG motor imagery classification with deep convolutional neural network
In recent years, deep learning has emerged as a powerful tool for developing Brain–
Computer Interface (BCI) systems. However, for deep learning models trained entirely on the …
Computer Interface (BCI) systems. However, for deep learning models trained entirely on the …
[HTML][HTML] A mutli-scale spatial-temporal convolutional neural network with contrastive learning for motor imagery EEG classification
Motor imagery (MI) based Brain-computer interfaces (BCIs) have a wide range of
applications in the stroke rehabilitation field. However, due to the low signal-to-noise ratio …
applications in the stroke rehabilitation field. However, due to the low signal-to-noise ratio …
Sequential Transfer Learning via segment after cue enhances the motor imagery-based brain-computer interface
Brain-computer interface (BCI) based on electroencephalogram (EEG) is a promising
technology, allowing computers to estimate human intentions. Intention recognition tool such …
technology, allowing computers to estimate human intentions. Intention recognition tool such …
A hybrid transfer learning approach for motor imagery classification in brain-computer interface
The classification of motor imagery (MI) signal is a representative problem in brain-computer
interface (BCI) systems. Because one main application field of MI-based BCI is medical …
interface (BCI) systems. Because one main application field of MI-based BCI is medical …
Deep and wide transfer learning with kernel matching for pooling data from electroencephalography and psychological questionnaires
DF Collazos-Huertas, LF Velasquez-Martinez… - Sensors, 2021 - mdpi.com
Motor imagery (MI) promotes motor learning and encourages brain–computer interface
systems that entail electroencephalogram (EEG) decoding. However, a long period of …
systems that entail electroencephalogram (EEG) decoding. However, a long period of …
A multi-scale fusion CNN model based on adaptive transfer learning for multi-class MI-classification in BCI system
AM Roy - BioRxiv, 2022 - biorxiv.org
Deep learning-based brain-computer interface (BCI) in motor imagery (MI) has emerged as
a powerful method for establishing direct communication between the brain and external …
a powerful method for establishing direct communication between the brain and external …
Transfer learning with data alignment and optimal transport for EEG based motor imagery classification
C Chu, L Zhu, A Huang, P Xu, N Ying… - Journal of Neural …, 2024 - iopscience.iop.org
Objective. The non-stationarity of electroencephalogram (EEG) signals and the variability
among different subjects present significant challenges in current Brain–Computer …
among different subjects present significant challenges in current Brain–Computer …
Cross-subject & cross-dataset subject transfer in motor imagery bci systems
T Zaremba, A Atyabi - 2022 International Joint Conference on …, 2022 - ieeexplore.ieee.org
Motor Imagery (MI) based Brain Computer Interfaces (BCIs) are seen as effective
mechanisms for motor rehabilitation. Aside from promises of MI based BCI systems, their …
mechanisms for motor rehabilitation. Aside from promises of MI based BCI systems, their …
A deep transfer learning network with two classifiers based on sample selection for motor imagery brain-computer interface
M Zheng, Y Lin - Biomedical Signal Processing and Control, 2024 - Elsevier
The non-stationary of Motor Imagery (MI) electroencephalogram (EEG) signals makes the
traditional machine learning methods ineffective in EEG recognition across time, which limits …
traditional machine learning methods ineffective in EEG recognition across time, which limits …