Abstract
Epilepsy is a common neurological disorder which affects 0.5-1% of the world population. Its diagnosis relies both on Electroencephalogram (EEG) findings and characteristic seizure -induced body movements - called seizure semiology. Thus, synchronous EEG and (2D) video recording systems (known as Video-EEG) are the most accurate tools for epilepsy diagnosis. Despite the establishment of several quantitative methods for EEG analysis, seizure semiology is still analyzed by visual inspection, based on epileptologists' subjective interpretation of the movements of interest (MOIs) that occur during recorded seizures. In this contribution, we present NeuroKinect, a low-cost, easy to setup and operate solution for a novel 3Dvideo-EEG system. It is based on a RGB-D sensor (Microsoft Kinect camera) and performs 24/7 monitoring of an Epilepsy Monitoring Unit (EMU) bed. It does not require the attachment of any reflectors or sensors to the patient's body and has a very low maintenance load. To evaluate its performance and usability, we mounted a state-of-the-art 6-camera motion-capture system and our low-cost solution over the same EMU bed. A comparative study of seizure-simulated MOIs showed an average correlation of the resulting 3D motion trajectories of 84.2%. Then, we used our system on the routine of an EMU and collected 9 different seizures where we could perform 3D kinematic analysis of 42 MOIs arising from the temporal (TLE) (n = 19) and extratemporal (ETE) brain regions (n = 23). The obtained results showed that movement displacement and movement extent discriminated both seizure MOI groups with statistically significant levels (mean = 0.15 m vs. 0.44 m, p<0.001; mean = 0.068 m(3) vs. 0.14 m(3), p< 0.05, respectively). Furthermore, TLE MOIs were significantly shorter than ETE (mean = 23 seconds vs 35 seconds, p< 0.01) and presented higher jerking levels (mean = 345 ms(-3) vs 172 ms(-3), p< 0.05). Our newly implemented 3D approach is faster by 87.5% in extracting body motion trajectories when compared to a 2D frame by frame tracking procedure. We conclude that this new approach provides a more comfortable (both for patients and clinical professionals), simpler, faster and lower-cost procedure than previous approaches, therefore providing a reliable tool to quantitatively analyze MOI patterns of epileptic seizures in the routine of EMUs around the world. We hope this study encourages other EMUs to adopt similar approaches so that more quantitative information is used to improve epilepsy diagnosis.

Abstract
The success of neurosurgery strongly depends on the pre-neurosurgical evaluation phase, in which the delineation of the areas to be removed or to be stimulated must be very accurate. For patients undergoing Deep Brain Stimulation (DBS) it is vital the delineation of the target area prior to surgery, and after the implantation of the DBS lead to confirm the electrodes positioning. In this paper we present a system to accurately determine the 3D position of DBS electrodes implanted within the brain of Parkinson and Dystonia patients. The system was tested using a multimodal dataset from 16 patients (8 with Parkinson's disease and 8 with dystonia) and, on average, the differences between the detected electrodes positions and the ones estimated manually by an experienced physician were less than a voxel in all cases. Copyright

Abstract
In recent years, safer and more reliable biometric methods have been developed. Apart from the need for enhanced security, the media and entertainment sectors have also been applying biometrics in the emerging market of user-adaptable objects/systems to make these systems more user-friendly. However, the complexity of some state-of-the-art biometric systems (e.g., iris recognition) or their high false rejection rate (e.g., fingerprint recognition) is neither compatible with the simple hardware architecture required by reduced-size devices nor the new trend of implementing smart objects within the dynamic market of the Internet of Things (IoT). It was recently shown that an individual can be recognized by extracting features from their electrocardiogram (ECG). However, most current ECG-based biometric algorithms are computationally demanding and/or rely on relatively large (several seconds) ECG samples, which are incompatible with the aforementioned application fields. Here, we present a computationally low-cost method (patent pending), including simple mathematical operations, for identifying a person using only three ECG morphology-based characteristics from a single heartbeat. The algorithm was trained/tested using ECG signals of different duration from the Physionet database on more than 60 different training/test datasets. The proposed method achieved maximal averaged accuracy of 97.450% in distinguishing each subject from a ten-subject set and false acceptance and rejection rates (FAR and FRR) of 5.710 +/- 1.900% and 3.440 +/- 1.980%, respectively, placing Beat-ID in a very competitive position in terms of the FRR/FAR among state-of-the-art methods. Furthermore, the proposed method can identify a person using an average of 1.020 heartbeats. It therefore has FRR/FAR behavior similar to obtaining a fingerprint, yet it is simpler and requires less expensive hardware. This method targets low-computational/energy-cost scenarios, such as tiny wearable devices (e.g., a smart object that automatically adapts its configuration to the user). A hardware proof-of concept implementation is presented as an annex to this paper.

Abstract
Background: The GPi (globus pallidus internus) is an important target nucleus for Deep Brain Stimulation (DBS) in medically refractory movement disorders, in particular dystonia and Parkinson's disease. Beneficial clinical outcome critically depends on precise electrode localization. Recent evidence indicates that not only neurons, but also axonal fibre tracts contribute to promoting the clinical effect. Thus, stereotactic planning should, in the future, also take the individual course of fibre tracts into account. Objective: The aim of this project is to explore the GPi connectivity profile and provide a connectivity based parcellation of the GPi. Methods: Diffusion MRI sequences were performed in sixteen healthy, right-handed subjects. Connectivity-based parcellation of the GPi was performed applying two independent methods: 1) a hypothesis-driven, seed-to-target approach based on anatomic priors set as connectivity targets and 2) a purely data-driven approach based on k-means clustering of the GPi. Results: Applying the hypothesis-driven approach, we obtained five major parcellation clusters, displaying connectivity to the prefrontal cortex, the brainstem, the GPe (globus pallidus externus), the putamen and the thalamus. Parcellation clusters obtained by both methods were similar in their connectivity profile. With the data-driven approach, we obtained three major parcellation clusters. Inter individual variability was comparable with results obtained in thalamic parcellation. Conclusion: The three parcellation clusters obtained by the purely data-driven method might reflect GPi subdivision into a sensorimotor, associative and limbic portion. Clinical and physiological studies indicate greatest clinical DBS benefit for electrodes placed in the postero-ventro-lateral GPi, the region displaying connectivity to the thalamus in our study and generally attributed to the sensorimotor system. Clinical studies relating DBS electrode positions to our GPi connectivity map would be needed to complement our findings.

Abstract
Optical Tweezers (OT) are able to trap/manipulate dielectric particles with few microns in a contactless manner due to forces exerted on them by a strongly focused optical beam. OT are being applied in Biology/Medicine, especially Optical Fiber Tweezers (OFT), for being simpler and more flexible than the conventional setups. Despite of the trapping phenomena of symmetrical particles by OFTs being already modeled, effects regarding complex bodies remain poorly understood. Here we provide a 2D characterization of the trapping forces exerted by a laser OFT on a geometric form of a Red Blood Cell (RBC), occupying different positions in a grid, using the method proposed by Barnett&Loudon. Comparisons were made between the forces exerted on a RBC having the mean normal size; a RBC with 80% of the normal size and an 1.5µm circular particle, due to the size and shape variability of biological-derived structures. The influence of RBCs inclination angles regarding its major axis on trapping performance was also evaluated for angles of p/4 and p/2. Simulation results showed that trapping phenomena are possible for all the conditions evaluated, as well as calculated trapping forces range was according with the literature (pN). We observed that, despite of modeled particles having the same optical characteristics, features such as particle geometry, size, position and inclination degree influence trapping. Trapping forces magnitude was higher for RBC relatively to the circular symmetrical particle; for large RBCs than RBCs with smaller dimensions; and for inclined RBCs than erythrocytes horizontally aligned. Those results reinforce the importance of modeling optical experiments to determine relevant parameters which affect trapping performance. © 2017 IEEE.

Abstract
A computational method for optical fiber trapping of healthy and Malariainfected blood cells characterization is proposed. A trapping force relation with the infection stage was found, which could trigger the development of a diagnostic sensor. © OSA 2017.