In this primer, we give a review of the inverse problem for EEG source localization. This is intended for the researchers new in the field to get insight in the state-of-the-art techniques used to find approximate solutions of the brain sources giving rise to a scalp potential recording. Furthermore, a review of the performance results of the different techniques is provided to compare these different inverse solutions. The authors also include the results of a Monte-Carlo analysis which they performed to compare four non parametric algorithms and hence contribute to what is presently recorded in the literature. An extensive list of references to the work of other researchers is also provided. This paper starts off with a mathematical description of the inverse problem and proceeds to discuss the two main categories of methods which were developed to solve the EEG inverse problem, mainly the non parametric and parametric methods. The main difference between the two is to whether a fixed number of dipoles is assumed a priori or not. Various techniques falling within these categories are described including minimum norm estimates and their generalizations, LORETA, sLORETA, VARETA, S-MAP, ST-MAP, Backus-Gilbert, LAURA, Shrinking LORETA FOCUSS (SLF), SSLOFO and ALF for non parametric methods and beamforming techniques, BESA, subspace techniques such as MUSIC and methods derived from it, FINES, simulated annealing and computational intelligence algorithms for parametric methods. From a review of the performance of these techniques as documented in the literature, one could conclude that in most cases the LORETA solution gives satisfactory results. In situations involving clusters of dipoles, higher resolution algorithms such as MUSIC or FINES are however preferred. Imposing reliable biophysical and psychological constraints, as done by LAURA has given superior results. The Monte-Carlo analysis performed, comparing WMN, LORETA, sLORETA and SLF, for different noise levels and different simulated source depths has shown that for single source localization, regularized sLORETA gives the best solution in terms of both localization error and ghost sources. Furthermore the computationally intensive solution given by SLF was not found to give any additional benefits under such simulated conditions.

Background: The aim of electroencephalogram (EEG) source localization is to find the brain areas responsible for EEG waves of interest. It consists of solving forward and inverse problems. The forward problem is solved by starting from a given electrical source and calculating the potentials at the electrodes. These evaluations are necessary to solve the inverse problem which is defined as finding brain sources which are responsible for the measured potentials at the EEG electrodes.

Testing and implementation of Human-Robot Collaboration (HRC) could be dangerous due to the high-speed movements and massive forces generated by industrial robots. Wherever humans and industrial robots share a common workplace, accidents are likely to happen and always unpredictable. This has hindered the development of human robot collaborative strategies as well as the ability of authorities to pass regulations on how humans and robots should work together in close proximities. This paper presents the use of a Virtual Reality digital twin of a physical layout as a mechanism to understand human reactions to both predictable and unpredictable robot motions. A set of established metrics as well as a newly developed Kinetic Energy Ratio metric are used to analyse human reactions and validate the effectiveness of the Virtual Reality environment. It is the aim that Virtual Reality digital twins could inform the safe implementation of Human-Robot Collaborative strategies in factories of the future.

Brain calcifications are a common radiographic finding. The pathogenesis is diverse and ranges from benign physiological calcifications to a variety of pathological disorders. Whereas certain calcifications are considered an incidental finding, their presence can sometimes be crucial in making a specific diagnosis. Several pathological conditions affecting the brain parenchyma are associated with calcifications and their recognition and location might help in narrowing the differential. Knowledge of physiological calcifications is essential to avoid misinterpretation. This review illustrates a broad spectrum of CNS disorders associated with calcifications, and tries to highlight the salient radiological findings.

One of the most used Position, Navigation and Timing (PNT) technology of the 21st century is Global Navigation Satellite Systems (GNSS). GNSS signals are affected by urban canyons that limit line-of-sight and reduce satellite availability to receivers. Smart cities are expected to adopt autonomous Unmanned Aerial Vehicles (UAV) operations for critical missions such as transportation of organs which are time-sensitive. Therefore, higher accuracy for position and velocity information is required. This paper investigates the use of Gated Recurrent Units (GRU) as a suitable technique that can memorize previous information in conjunction with the inputs (consisting of attitude, change in attitude, and change in velocity) to reduce position and velocity error when GNSS is not available. The fusion approach is developed and tested using Spirent's SimGEN GSS7000 hardware simulator which simulates GNSS signals and Spirent's SimSENSOR software that simulates accelerometer and gyroscope stochastic and deterministic errors. GNSS outage is varied between 1 and 20 seconds randomly to affect predicted position and velocity. The data is collected and used to train the GRU to predict the position and velocity error measured by the Inertial Measurement Unit (IMU). From the performance evaluation, a 60% reduction in Root Mean Squared Error (RMSE) is observed compared to Recurrent Neural Networks (RNN). Comparing 95 th percentile with Inertial Navigation System (INS), RNN, and GRU, an 80% reduction is observed between INS and RNN. Furthermore, a 35% drop in the 95 th percentile is observed between RNN and GRU.

This paper proposes a system for effecting trajectory tracking in combination with obstacle avoidance in mobile robotic systems. In robotics research, these two situations are typically considered as separate problems. This work approaches the problem by integrating classical trajectoryfollowing control schemes with Kim et al.'s Limit Cycle Navigation method for obstacle avoidance. The use of Artificial Potential Function methods for obstacle avoidance is purposely avoided so as to prevent the well-known problems of local minima associated with such schemes. The paper also addresses the problem of non-global obstacle sensing and proposes modifications to Kim et al.'s method for handling multiple, overlapping obstacles under local sensing conditions.

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