  Dementia is a syndrome characterised by the decline of different cognitive
abilities. Alzheimer's Disease (AD) is the most common dementia affecting
cognitive domains such as memory and learning, perceptual-motion or executive
function. High rate of deaths and high cost for detection, treatments and
patient's care count amongst its consequences. Early detection of AD is
considered of high importance for improving the quality of life of patients and
their families. The aim of this thesis is to introduce novel non-invasive early
diagnosis methods in order to speed the diagnosis, reduce the associated costs
and make them widely accessible. Novel AD's screening tests based on virtual
environments using new immersive technologies combined with advanced Human
Computer Interaction (HCI) systems are introduced. Four tests demonstrate the
wide range of screening mechanisms based on cognitive domain impairments that
can be designed using virtual environments. The use of emotion recognition to
analyse AD symptoms has been also proposed. A novel multimodal dataset was
specifically created to remark the autobiographical memory deficits of AD
patients. Data from this dataset is used to introduce novel descriptors for
Electroencephalogram (EEG) and facial images data. EEG features are based on
quaternions in order to keep the correlation information between sensors,
whereas, for facial expression recognition, a preprocessing method for motion
magnification and descriptors based on origami crease pattern algorithm are
proposed to enhance facial micro-expressions. These features have been proved
on classifiers such as SVM and Adaboost for the classification of reactions to
autobiographical stimuli such as long and short term memories.
