Abstract
Movement quantification of the human body is presently used for analyzing deficits resulting from Central Nervous System (CNS) pathologies or exploring the insights of the human motor system behaviour. Following our previous work on 2D movement quantification of epileptic seizures, we now present a feasibility study for a newly developed 3D technique. In order to validate this new 3D approach we made a comparison with the previous method. Both techniques were tested in two different datasets: a simple motor execution performed by a volunteer and a complex motor motion induced by a real epileptic seizure. The results obtained showed, as expected, the superior robustness and precision of the 3D approach but also confirmed the validity of the 2D method, given certain constraints. We conclude that the newly developed 3D system will highly improve our capacity of pursuing the clinical research on quantitative characterization of seizure semiology to support epilepsy diagnosis.

Abstract
Every day, thousands of first responders work to save the lives of others, sometimes without the adequate surveillance of health conditions. The VitalResponder is a project that aims at monitoring and control teams of first responders in emergency scenarios, using mobile technologies to capture and use real-time data to support real-time coordination. In this paper we present a system to capture, process, and display the vital signs of team members, which are made available to a first responders' team leader, for coordination and monitoring. The system addresses specific requirements of the field action, such as the mobility of actors, combining two of the most recent mobile technologies: the iPad (for the coordination view) and Android OS-based smartphones (for real-time sensor data acquisition). © 2012 ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering.

Abstract
We propose a technical solution that enables 3D video-based in-bore movement quantification to be acquired synchronously with the BOLD function magnetic resonance imaging (fMRI) sequences. Our solution relies on in-bore video setup with 2 cameras mounted in a 90 degrees angle that allows tracking movments while acquiring fMRI sequences. In this study we show that using 3D motion quantification of a simple finger opposition paradigm we were able to map two different finger positions to two different BOLD response patterns in a typical block design protocol. The motion information was also used to adjust the block design to the actual motion start and stop improving the time accuracy of the analysis. These results reinforce the role of video based motion quantification in fMRI analysis as an independent regressor that allows new findings not discernable when using traditional block designs.

Abstract
The combination of electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) is a powerful tool to study brain function. In this study, we present a video-EEG-fMRI system where in-bore video, EEG and fMRI are acquired synchronously. To determine the added value of video in a typical EEG-fMRI scenario, we analyzed a simple motor activation paradigm (right index tapping). By using in-bore video, our results show that it is possible to determine different EEG potentials related to motion as well as to clearly distinguish the corresponding blood oxygen level dependent activations.

Abstract
EpiGauss is a method that combines single dipole model with dipole clustering to characterize active brain generators in space and time related to EEG events. EpiGauss was applied to study epileptogenic activity in 4 patients suffering of hypothalamic hamartoma related epilepsy, a rare syndrome with a unique epileptogenic source - the hamartoma lesion - and natural propagation hypothesis - from hamartoma to the surface EEG focus. The results are compared to Rap-MUSIC and Single Moving Dipole methods over the same patients. © Springer-Verlag Berlin Heidelberg 2006.

Abstract
Functional magnetic resonance imaging (fMRI) is an imaging technique that can be used to characterize brain physiological activity, usually presented as 3D volumes in function of time. In the context of our previous work in nonlinear association studies in electroencephalogram (EEG) time series, we were able to identify clinical relevant features useful in clinical diagnosis. The use of a similar approach in fMRI, now adapted for 3D time series, is both appealing and flew. Such time series analysis imposes challenging requirements regarding computational power and medical image management. In this paper we propose a grid architecture framework to support the typical analysis protocol of association analysis applied to fMRI. The system, implemented using the gLite middleware, provides the necessary support to manage brain images and run non-linear fMRI analysis methods.