Demystifying supervised learning in healthcare 4.0: A new reality of transforming diagnostic medicine
The global healthcare sector continues to grow rapidly and is reflected as one of the fastest-
growing sectors in the fourth industrial revolution (4.0). The majority of the healthcare …
growing sectors in the fourth industrial revolution (4.0). The majority of the healthcare …
[HTML][HTML] Cognitive neuroscience and robotics: Advancements and future research directions
In recent years, brain-based technologies that capitalise on human abilities to facilitate
human–system/robot interactions have been actively explored, especially in brain robotics …
human–system/robot interactions have been actively explored, especially in brain robotics …
Finger pinching and imagination classification: A fusion of CNN architectures for IoMT-enabled BCI applications
Abstract A Brain–Computer Interface (BCI), integrated with the Internet of Medical Things
(IoMT) and based on electroencephalogram (EEG) technology, allows users to control …
(IoMT) and based on electroencephalogram (EEG) technology, allows users to control …
Eeg-based alzheimer's disease recognition using robust-pca and lstm recurrent neural network
M Alessandrini, G Biagetti, P Crippa, L Falaschetti… - Sensors, 2022 - mdpi.com
The use of electroencephalography (EEG) has recently grown as a means to diagnose
neurodegenerative pathologies such as Alzheimer's disease (AD). AD recognition can …
neurodegenerative pathologies such as Alzheimer's disease (AD). AD recognition can …
Automatic seizure detection by convolutional neural networks with computational complexity analysis
Abstract Background and Objectives Nowadays, an automated computer-aided diagnosis
(CAD) is an approach that plays an important role in the detection of health issues. The main …
(CAD) is an approach that plays an important role in the detection of health issues. The main …
Classification of tumor in brain MR images using deep convolutional neural network and global average pooling
Brain tumors can cause serious health complications and lead to death if not detected
accurately. Therefore, early-stage detection of brain tumors and accurate classification of …
accurately. Therefore, early-stage detection of brain tumors and accurate classification of …
Pre-training in medical data: A survey
Medical data refers to health-related information associated with regular patient care or as
part of a clinical trial program. There are many categories of such data, such as clinical …
part of a clinical trial program. There are many categories of such data, such as clinical …
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 …
Recent trends in EEG based Motor Imagery Signal Analysis and Recognition: A comprehensive review.
The electroencephalogram (EEG) motor imagery (MI) signals are the widespread paradigms
in the brain-computer interface (BCI). Its significant applications in the gaming, robotics, and …
in the brain-computer interface (BCI). Its significant applications in the gaming, robotics, and …
Early detection of paediatric and adolescent obsessive–compulsive, separation anxiety and attention deficit hyperactivity disorder using machine learning algorithms
Purpose Mental health issues of young minds are at the threshold of all development and
possibilities. Obsessive–compulsive disorder (OCD), separation anxiety disorder (SAD), and …
possibilities. Obsessive–compulsive disorder (OCD), separation anxiety disorder (SAD), and …