Electroencephalogram (EEG) plays an important role in identifying brain activity and
behavior. However, the recorded electrical activity always be contaminated with artifacts and …

Electromyography (EMG) is gaining importance in many research and clinical applications,
including muscle fatigue detection, control of robotic mechanisms and prostheses, clinical …

The combination of reducing birth rate and increasing life expectancy continues to drive the
demographic shift toward an aging population. This, in turn, places an ever-increasing …

Biosignal measurement and processing is increasingly being deployed in ambulatory
situations particularly in connected health applications. Such an environment dramatically …

The problem of source separation is an inductive inference problem. There is not enough
information to deduce the solution, so one must use any available information to infer the …

Surface electromyograms (EMGs) are valuable in the pathophysiological study and clinical
treatment for dystonia. These recordings are critically often contaminated by cardiac artefact …

Recent advancements in signal processing and computerised methods are expected to
underpin the future progress of biomedical research and technology, particularly in …

The electroencephalogram (EEG) signals represent the electrical activity of the brain. In
applications, such as brain-computer interface (BCI), features of the EEG signals are used to …

Electroencephalogram (EEG) is the record of cerebral activity, the electric potential of
cerebral activity is of low amplitude, and less frequency ranges between 4 to 60 Hz, which …

Industrial Internet of Things (IIoT) and its applications have seen a paradigm shift since the
advent of artificial intelligence and machine learning. However, these methods are mostly …