Alcohol affects the brain's resting-state network in social drinkers.

Acute alcohol intake is known to enhance inhibition through facilitation of GABA(A) receptors, which are present in 40% of the synapses all over the brain. Evidence suggests that enhanced GABAergic transmission leads to increased large-scale brain connectivity. Our hypothesis is that acute alcohol intake would increase the functional connectivity of the human brain resting-state network (RSN).

Real time emotion aware applications: a case study employing emotion evocative pictures and neuro-physiological sensing enhanced by Graphic Processor Units.

In this paper the feasibility of adopting Graphic Processor Units towards real-time emotion aware computing is investigated for boosting the time consuming computations employed in such applications. The proposed methodology was employed in analysis of encephalographic and electrodermal data gathered when participants passively viewed emotional evocative stimuli. The GPU effectiveness when processing electroencephalographic and electrodermal recordings is demonstrated by comparing the execution time of chaos/complexity analysis through nonlinear dynamics (multi-channel correlation dimension/D2) and signal processing algorithms (computation of skin conductance level/SCL) into various popular programming environments.

Toward emotion aware computing: an integrated approach using multichannel neurophysiological recordings and affective visual stimuli.

This paper proposes a methodology for the robust classification of neurophysiological data into four emotional states collected during passive viewing of emotional evocative pictures selected from the International Affective Picture System. The proposed classification model is formed according to the current neuroscience trends, since it adopts the independency of two emotional dimensions, namely arousal and valence, as dictated by the bidirectional emotion theory, whereas it is gender-specific. A two-step classification procedure is proposed for the discrimination of emotional states between EEG signals evoked by pleasant and unpleasant stimuli, which also vary in their arousal/intensity levels.

On the classification of emotional biosignals evoked while viewing affective pictures: an integrated data-mining-based approach for healthcare applications.

Recent neuroscience findings demonstrate the fundamental role of emotion in the maintenance of physical and mental health. In the present study, a novel architecture is proposed for the robust discrimination of emotional physiological signals evoked upon viewing pictures selected from the International Affective Picture System (IAPS). Biosignals are multichannel recordings from both the central and the autonomic nervous systems.

An automated system for processing electrodermal activity.

A new approach is presented in this paper for the display and processing of electrodermal activity. It offers a fully automated interface for the pre-processing and scoring individual skin conductance responses (SCRs). The application supports parallel processing by means of multiple threads.

Towards a semantic framework for an integrative description of neuroscience patterns and studies: a case for emotion-related data.

The continuously increasing number of neuroscience studies and the difficulties associated with searching for related information and properly tracking neuroscience findings makes it imperative that one may be lead to isolated theories and findings which may be incompatible to each other or partially occluded. Semantically describing several aspects of studies in this field, such as, research groups attributes, aims of studies, experimental procedures followed, hardware and software tools utilised, acquisition systems used, as well as, the emerging neuro-physiological patterns found, may facilitate an integrative view of neuroscience theories. To this end, the current piece of work aims to provide a global theoretical framework using ontologies and semantic rules to describe neuroscience studies.

A framework combining delta Event-Related Oscillations (EROs) and Synchronisation Effects (ERD/ERS) to study emotional processing.

Event-Related Potentials (ERPs) or Event-Related Oscillations (EROs) have been widely used to study emotional processing, mainly on the theta and gamma frequency bands. However, the role of the slow (delta) waves has been largely ignored. The aim of this study is to provide a framework that combines EROs with Event-Related Desynchronization (ERD)/Event-Related Synchronization (ERS), and peak amplitude analysis of delta activity, evoked by the passive viewing of emotionally evocative pictures.

A methodology for reliability analysis in health networks.

A reliability model for a health care domain based on requirement analysis at the early stage of design of regional health network (RHN) is introduced. RHNs are considered as systems supporting the services provided by health units, hospitals, and the regional authority. Reliability assessment in health care domain constitutes a field-of-quality assessment for RHN.

Enhancing the quality of phylogenetic analysis using fuzzy hidden Markov model alignments.

Any effective phylogeny inference based on molecular data begins by performing efficient multiple sequence alignments. So far, the Hidden Markov Model (HMM) method for multiple sequence alignment has been proved competitive to the classical deterministic algorithms with respect to phylogenetic analysis; nevertheless, its stochastic nature does not help it cope with the existing dependence among the sequence elements. This paper deals with phylogenetic analysis of protein and gene data using multiple sequence alignments produced by fuzzy profile Hidden Markov Models.

Multi-channel physiological sensing of human emotion: insights into emotion-aware computing using affective protocols, avatars and emotion specifications.

This paper introduces a methodology for combining multi-channel psycho-physiological recordings of affective paradigms into a framework where the scientific results of such experiments are utilized in the human computer interaction context to model the computer's response based on the emotional context of the user and the situation. An affective protocol is described the results of which are expected to be combined with anthropomorphic avatars that enhance the man-machine interaction. The technological infrastructure of the later component is provided by means of XML specifications of signal descriptions and emotion recognition, as well as avatar behavior generator descriptions.

Reliability assessment of home health care services.

In this paper, a model of reliability assessment of services in Home Health Care Delivery is presented. Reliability is an important quality dimension for services and is included in non-functional requirements of a system. A stochastic Markov model for reliability assessment is applied to patient communication services, in the field of home health care delivery.

Fuzzy Hidden Markov Models: a new approach in multiple sequence alignment.

This paper proposes a novel method for aligning multiple genomic or proteomic sequences using a fuzzyfied Hidden Markov Model (HMM). HMMs are known to provide compelling performance among multiple sequence alignment (MSA) algorithms, yet their stochastic nature does not help them cope with the existing dependence among the sequence elements. Fuzzy HMMs are a novel type of HMMs based on fuzzy sets and fuzzy integrals which generalizes the classical stochastic HMM, by relaxing its independence assumptions.

DIABCARD CCMIS--a portable and scalable CPR for diabetes care.

The aim of this paper is to introduce the main software module of the DIABCARD Chip Card Medical Information System (DIABCARD CCMIS) that provides an online, portable diabetes medical record information system based on a high performance object-oriented rapid application development language such as Borland Delphi. A chip card based medical information system was developed as a good possibility to create a portable electronic patient record. In particular the patient data card makes the up-to-date patient's record available whenever needed.

Model-based processing scheme for quantitative 4-D cardiac MRI analysis.

In this paper, we present an integrated model-based processing scheme for cardiac magnetic resonance imaging (MRI), embedded in an interactive computing environment suitable for quantitative cardiac analysis, which provides a set of functions for the extraction, modeling, and visualization of cardiac shape and deformation. The methods apply four-dimensional (4-D) processing (three spatial and one temporal) to multiphase multislice MRI acquisitions and produce a continuous 4-D model of the myocardial surface deformation. The model is used to measure diagnostically useful parameters, such as wall motion, myocardial thicking, and myocardial mass measurements.