Abstract
The design and testing of a "dry" active electrode for electroencephalographic recording is described. A comparative study between the EEG signals recorded in human volunteers simultaneously with the classical Ag-AgCl and "dry" active electrodes was carried out and the reported preliminary results are consistent with a better performance of these devices over the conventional Ag-AgCl electrodes.

Abstract
It is common that epileptic seizures induce uncoordinated movement in a patient's body. This movement is a relevant clinical factor in seizure identification. Nevertheless, quantification of this information has not been an object of much attention from the scientific community. In this paper, we present our effort in developing a new approach to the quantification of movement patterns in patients during epileptic seizures. We attach markers at landmark points of a patient's body and use a camera and a commercial video-electroencephalogram (EEG) system to synchronously register EEG and video during seizures. Then, we apply image-processing techniques to analyze the video frames and extract the trajectories of those points that represent the course of the quantified movement of different body parts. This information may help clinicians in seizure classification. We describe the framework of our system and a method of analyzing video in order to achieve the proposed goal. Our experimental results show that our method can reflect quantified motion patterns of epileptic seizures, which cannot be accessed by means of traditional visual inspection of video recordings. We were able, for the first time, to quantify the movement of different parts of a convulsive human body in the course of an epileptic seizure. This result represents an enhanced value to clinicians in studying seizures for reaching a diagnosis.

Abstract
In this paper, we describe the design, implementation and testing of a dry active flexible electrode with a novel interface material for wearable biosignal recording. The new interface material takes the form of a gel and is highly bendable and comfortable on the wearer's skin. A comparison between common Ag/AgCl and our dry active electrode was performed on seven healthy volunteers. The presented prototype was designed for ECG signals but this technology can be modified for other biosignals. Our results show that the new dry active electrode presents better electrical characteristics than the common Ag/AgCl electrode, namely less power-line interference and better response in the signal band. We can conclude that our novel dry active flexible electrode outperforms the traditional Ag/AgCl wet electrode with the advantages of being dry and comfortable. Some future applications of this biodevice are discussed.

Abstract
Professionals such as First Responders are frequently exposed to extreme environmental conditions, which induce stress and fatigue during extensive periods of time. In this scenario, the main issues are the quantification and evaluation of stress and fatigue, since uncontrolled levels have a profound and negative impact on human health and performance. Based on an existing wearable monitoring solution - the Vital Jacket (R)- we propose an individual and team monitoring mobile solution called DroidJacket. DroidJacket is based on Android mobile devices and provides data aggregation, processing, visualization and optionally relaying services. The DroidJacket design is plugin oriented, integrating analysis modules, namely an online ECG plugin for both real time pulse and arrhythmia detection.

Abstract
Recent advances in computer hardware and signal processing assert that controlling certain functions by thoughts may represent a landmark in the way we interact with many output devices. This paper exploits the possibility of achieving a communication channel between the brain and a mobile robot through the modulation of the electroencephalogram (EEG) signal during motor imagery tasks. A major concern was directed towards designing a generalized and multi-purpose framework that supports rapid prototyping of various experimental strategies and operating modes. Preliminary results of brain-state estimation using EEG signals recorded during a self-paced left/right hand movement task are also presented. The user successfully learned to operate the system and how to better perform the motor-related tasks based on outcomes produced by its mental focus.

Abstract
Neurofibromatosis type 1 (NF1) is a genetic disorder characterized by increased predisposition for tumor development and cognitive deficits. In this work, we used maps of grey matter density obtained from Magnetic Resonance (MR) brain structural scans to distinguish between NF1 patients and healthy controls with a multivariate pattern analysis technique, Support Vector Machines. Up to 83% of all participants were correctly classified (mean sensitivity of 82%; mean specificity of 84%; significance level p< 0.01). This high level of classification accuracy of NF1 patients suggests this technique as a potential diagnostic tool. In addition, we determined the brain regions that the algorithm used to distinguish between NF1 patients and healthy controls. These regions were not identified as abnormal using univariate voxel-by-voxel comparison indicating that multivariate techniques are a useful powerful tool with which to identify potential structural defects in the NF1 brain. © 2011 IEEE.