Status of deep learning for EEG-based brain–computer interface applications
In the previous decade, breakthroughs in the central nervous system bioinformatics and
computational innovation have prompted significant developments in brain–computer …
computational innovation have prompted significant developments in brain–computer …
Self-supervised learning for electroencephalography
Decades of research have shown machine learning superiority in discovering highly
nonlinear patterns embedded in electroencephalography (EEG) records compared with …
nonlinear patterns embedded in electroencephalography (EEG) records compared with …
[HTML][HTML] A Comprehensive Review on Brain–Computer Interface (BCI)-Based Machine and Deep Learning Algorithms for Stroke Rehabilitation
WH Elashmawi, A Ayman, M Antoun, H Mohamed… - Applied Sciences, 2024 - mdpi.com
This literature review explores the pivotal role of brain–computer interface (BCI) technology,
coupled with electroencephalogram (EEG) technology, in advancing rehabilitation for …
coupled with electroencephalogram (EEG) technology, in advancing rehabilitation for …
A fuzzy-enhanced deep learning approach for early detection of Covid-19 pneumonia from portable chest X-ray images
The Covid-19 pandemic is the defining global health crisis of our time. Chest X-Rays (CXR)
have been an important imaging modality for assisting in the diagnosis and management of …
have been an important imaging modality for assisting in the diagnosis and management of …
Uncertainty-guided voxel-level supervised contrastive learning for semi-supervised medical image segmentation
Semi-supervised learning reduces overfitting and facilitates medical image segmentation by
regularizing the learning of limited well-annotated data with the knowledge provided by a …
regularizing the learning of limited well-annotated data with the knowledge provided by a …
Multiple classification of brain MRI autism spectrum disorder by age and gender using deep learning
The fact that the rapid and definitive diagnosis of autism cannot be made today and that
autism cannot be treated provides an impetus to look into novel technological solutions. To …
autism cannot be treated provides an impetus to look into novel technological solutions. To …
AutoEncoder filter bank common spatial patterns to decode motor imagery from EEG
The present paper introduces a novel method, named AutoEncoder-Filter Bank Common
Spatial Patterns (AE-FBCSP), to decode imagined movements from electroencephalography …
Spatial Patterns (AE-FBCSP), to decode imagined movements from electroencephalography …
A new deep learning framework based on blood pressure range constraint for continuous cuffless BP estimation
Y Chen, D Zhang, HR Karimi, C Deng, W Yin - Neural Networks, 2022 - Elsevier
Blood pressure (BP) is known as an indicator of human health status, and regular
measurement is helpful for early detection of cardiovascular diseases. Traditional …
measurement is helpful for early detection of cardiovascular diseases. Traditional …
A novel multi-branch hybrid neural network for motor imagery EEG signal classification
W Ma, H Xue, X Sun, S Mao, L Wang, Y Liu… - … Signal Processing and …, 2022 - Elsevier
As a typical spontaneous brain-computer interface system, motor imagery has been widely
used in areas such as robot control and stroke rehabilitation. Recently, researchers have …
used in areas such as robot control and stroke rehabilitation. Recently, researchers have …
[HTML][HTML] EEG decoding method based on multi-feature information fusion for spinal cord injury
F Xu, J Li, G Dong, J Li, X Chen, J Zhu, J Hu, Y Zhang… - Neural Networks, 2022 - Elsevier
To develop an efficient brain–computer interface (BCI) system, electroencephalography
(EEG) measures neuronal activities in different brain regions through electrodes. Many EEG …
(EEG) measures neuronal activities in different brain regions through electrodes. Many EEG …