Abstract
The relevance of scalp EEG recording on the selection of patients for epilepsy surgery usually is based on the concordance between the location of the epileptogenic focus and the presumed ictal origin of clinical seizures visually identified or video recorded. Bilateralisation and/or spreading of the epileptiform events are among the causes of the relative lack of agreement between scalp topography and origin of EEG potentials. Computer methods mainly if they are adaptive and use multistrategic approaches improve the accuracy on the detection of the epileptogenic focus, extracting relevant information on the location and spreading of epileptiform events.

Abstract

Abstract
The usefulness of computer analysis of EEG is exemplified here by using it as an instrument for fine measurement of the EEG epileptiform events. By using a PC-AT compatible microcomputer and a software package developed to record, analyse and display EEG signals, topograms and brain maps of epileptiform events are drawn. The characterisation of events in the scalp near potential epileptic focus and on the neighbouring areas is done. Studies of the origin and spreading of these epileptiform events over the location (EEG foci changing) can be correlated, with the clinical characteristics and evolution of focal epilepsy.

Abstract
The advantages of Virtual Reality (VR) over traditional training, together with the development of VR technology, have contributed to an increase in the body of literature on training professionals with VR. However, there is a gap in the literature concerning the comparison of training in a Virtual Environment (VE) with the same training in a Real Environment (RE), which would contribute to a better understanding of the capabilities of VR in training. This paper presents a study with firefighters (N = 12) where the effect of a firefighter training exercise in a VE was evaluated and compared to that of the same exercise in a RE. The effect of environments was evaluated using psychophysiological measures by evaluating the perception of stress and fatigue, transfer of knowledge, sense of presence, cybersickness, and the actual stress measured through participants' Heart Rate Variability (HRV). The results showed a similar perception of stress and fatigue between the two environments; a positive, although not significant, effect of the VE on the transfer of knowledge; the display of moderately high presence values in the VE; the ability of the VE not to cause symptoms of cybersickness; and finally, obtaining signs of stress in participants' HRV in the RE and, to a lesser extent, signs of stress in the VE. Although the effect of the VE was shown to be non-comparable to that of the RE, the authors consider the results encouraging and discuss some key factors that should be addressed in the future to improve the results of the training VE.

Abstract
Many clinical applications involve in-bed patient activity monitoring, from intensive care and neuro-critical infirmary, to semiology-based epileptic seizure diagnosis support or sleep monitoring at home, which require accurate recognition of in-bed movement actions from video streams. The major challenges of clinical application arise from the domain gap between common in-the-lab and clinical scenery (e.g. viewpoint, occlusions, out-of-domain actions), the requirement of minimally intrusive monitoring to already existing clinical practices (e.g. non-contact monitoring), and the significantly limited amount of labeled clinical action data available. Focusing on one of the most demanding in-bed clinical scenarios - semiology-based epileptic seizure classification - this review explores the challenges of video-based clinical in-bed monitoring, reviews video-based action recognition trends, monocular 3D MoCap, and semiology-based automated seizure classification approaches. Moreover, provides a guideline to take full advantage of transfer learning for in-bed action recognition for quantified, evidence-based clinical diagnosis support. The review suggests that an approach based on 3D MoCap and skeleton-based action recognition, strongly relying on transfer learning, could be advantageous for these clinical in-bed action recognition problems. However, these still face several challenges, such as spatio-temporal stability, occlusion handling, and robustness before realizing the full potential of this technology for routine clinical usage.