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Electrocardiogram is a useful diagnostic signal that can detect cardiac abnormalities by measuring the electrical activity generated by the heart. Due to its rapid, non-invasive, and richly informative characteristics, ECG has many emerging…
This paper presents a novel Electrodermal Activity (EDA) signal acquisition system, designed to address the challenges of stress monitoring in contemporary society, where stress affects one in four individuals. Our system focuses on…
Background In vivo mechanical behaviour of the abdominal wall has been poorly characterised and important details are missing regarding the occurrence and post-operative recurrence rate of hernias which can be as high as 30 %. This study…
Classification of electroencephalogram (EEG) and electrocorticogram (ECoG) signals obtained during motor imagery (MI) has substantial application potential, including for communication assistance and rehabilitation support for patients with…
Continuous ambulatory monitoring of peripheral vascular perfusion could enable earlier detection of vascular dysfunction in individuals with diabetes mellitus and more timely management of cardiovascular disease. Clinical imaging modalities…
Magnetoencephalography using optically pumped magnetometers (OPM-MEG) is gaining significant traction as a neuroimaging tool, with the potential for improved performance and practicality compared to conventional instrumentation. However,…
There has been a long history in electrical muscle stimulation (EMS), which has been used for medical and interaction purposes. Human-computer interaction (HCI) researchers are now working on various applications, including virtual reality…
Electrical and elasticity imaging are promising modalities for a suite of different applications including medical tomography, non-destructive testing, and structural health monitoring. These emerging modalities are capable of providing…
The electrocardiogram (ECG) is a cost-effective, highly accessible and widely employed diagnostic tool. With the advent of Foundation Models (FMs), the field of AI-assisted ECG interpretation has begun to evolve, as they enable model reuse…
A limiting factor towards the wide routine use of wearables devices for continuous healthcare monitoring is their cumbersome and obtrusive nature. This is particularly true for electroencephalography (EEG) recordings, which require the…
In this study, we introduce an innovative EEG signal reconstruction sub-module designed to enhance the performance of deep learning models on EEG eye-tracking tasks. This sub-module can integrate with all Encoder-Classifier-based deep…
Concurrent transcranial magnetic stimulation (TMS) and electroencephalography (EEG), or TMS-EEG, holds the potential to broaden the clinical applications of TMS beyond its traditional role in evaluating the cortico-spinal tract and motor…
Deep convolutional neural networks (CNNs), especially fully convolutional networks, have been widely applied to automatic medical image segmentation problems, e.g., multi-organ segmentation. Existing CNN-based segmentation methods mainly…
Intracranial EEG (iEEG) is the gold standard technique for epileptogenic zone (EZ) localisation, but requires a hypothesis of which tissue is epileptogenic, guided by qualitative analysis of seizure semiology and other imaging modalities…
Electroencephalogram (EEG) signals may get easily contaminated by muscle artifacts, which may lead to wrong interpretation in the brain--computer interface (BCI) system as well as in various medical diagnoses. The main objective of this…
Restoring limb motor function in individuals with spinal cord injury (SCI), stroke, or amputation remains a critical challenge, one which affects millions worldwide. Recent studies show through surface electromyography (EMG) that spared…
Learning a general motion tracking policy from human motions shows great potential for versatile humanoid whole-body control. Conventional approaches are not only inefficient in data utilization and training processes but also exhibit…
Health professionals extensively use Two- Dimensional (2D) Ultrasound (US) videos and images to visualize and measure internal organs for various purposes including evaluation of muscle architectural changes. US images can be used to…
Electromyography (EMG) refers to a biomedical signal indicating neuromuscular activity and muscle morphology. Experts accurately diagnose neuromuscular disorders using this time series. Modern data analysis techniques have recently led to…
Neural wearables can enable life-saving drowsiness and health monitoring for pilots and drivers. While existing in-cabin sensors may provide alerts, wearables can enable monitoring across more environments. Current neural wearables are…