Giorgos A. Giannakakis

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The wavelet entropy (WE) of rest electroencephalogram (EEG) and of event-related potentials (ERP) carries information about the degree of order or disorder associated with a multi-frequency brain electrophysiological activity. In the present study, WE, relative WE and WE change were estimated for the EEG and ERP signals recorded during a working memory(More)
Monitoring of physiological signals of an individual via remote and contactless means is an important scientific challenge, whose resolution will enable the development of novel, nonintrusive mHealth and wellness-management systems and services. In this paper, the performance of three blind source separation algorithms for the optical estimation of the(More)
Epilepsy is one of the most common neurological diseases and the most common neurological chronic disease in childhood. Electroencephalography (EEG) still remains one of the most useful and effective tools in understanding and treatment of epilepsy. To this end, many computational methods have been developed for both the detection and prediction of(More)
In the recent years personal health monitoring systems have been gaining popularity, both as a result of the pull from the general population, keen to improve well-being and early detection of possibly serious health conditions and the push from the industry eager to translate the current significant progress in computer vision and machine learning into(More)
Epilepsy is one of the most common chronic neurological diseases and the most common neurological chronic disease of childhood. The electroencephalogram (EEG) signal provides significant information neurologists take into consideration in the investigation and analysis of epileptic seizures. The Approximate Entropy (ApEn) is a formulated statistical(More)
Recent research has implicated deficits of the working memory (WM) and attention in dyslexia. The N100 component of event-related potentials (ERP) is thought to reflect attention and working memory operation. However, previous studies showed controversial results concerning the N100 in dyslexia. Variability in this issue may be the result of inappropriate(More)
The understanding of the mechanisms of human brain is a demanding issue for neuroscience research. Physiological studies acknowledge the usefulness of synchronization coupling in the study of dysfunctions associated with reading difficulties. Magnetoencephalogram (MEG) is a useful tool towards this direction having been assessed for its superior accuracy(More)
The face reveals the healthy status of an individual, through a combination of physical signs and facial expressions. The project SEMEOTICONS is translating the semeiotic code of the human face into computational descriptors and measures, automatically extracted from videos, images, and 3D scans of the face. SEMEOTICONS is developing a multisensory(More)
In this paper, we address the problem of time-varying causal connectivity estimators on Electro-encephalographic (EEG) signals by means of Directed Transfer Function (DTF). The DTF method reveals causal information flows between brain areas, while direct DTF (dDTF) is able to distinguish and estimate only direct flows. Since neuro-physiological signals such(More)
Stress and anxiety act as psycho-physical factors that increase the risk of developing several chronic diseases. Since they appear as early indicators, it is very important to be able to perform their evaluation in a contactless and non-intrusive manner in order to avoid inducing artificial stress or anxiety to the individual in question. For these reasons,(More)