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.