Activity detection and classification using different sensor modalities have emerged as
revolutionary technology for real-time and autonomous monitoring in behaviour analysis …

Brain-Computer Interface (BCI) bridges human's neural world and the outer physical world
by decoding individuals' brain signals into commands recognizable by computer devices …

Many classical machine learning techniques have been used to explore Alzheimer's
disease (AD), evolving from image decomposition techniques such as principal component …

Most approaches to machine learning from electronic health data can only predict a single
endpoint. The ability to simultaneously simulate dozens of patient characteristics is a crucial …

This paper describes the analysis of a deep neural network for the classification of epileptic
EEG signals. The deep learning architecture is made up of two convolutional layers for …

An important subfield of brain–computer interface is the classification of motor imagery (MI)
signals where a presumed action, for example, imagining the hands' motions, is mentally …

The explosion of digital healthcare data has led to a surge of data-driven medical research
based on machine learning. In recent years, as a powerful technique for big data, deep …

Background Hippocampus is one of the first structures affected by neurodegenerative
diseases such as Alzheimer's disease (AD) and mild cognitive impairment (MCI) …

Early identification of dementia at the stage of mild cognitive impairment (MCI) is crucial for
timely diagnosis and intervention of Alzheimer's disease (AD). Although several pioneering …

Background EEG signals obtained from Mild Cognitive Impairment (MCI) and the
Alzheimer's disease (AD) patients are visually indistinguishable. New method A new …