Exploring the origins of EEG motion artefacts during simultaneous fMRI acquisition: implications for motion artefact correction

Motion artefacts (MAs) are induced within EEG data collected simultaneously with fMRI when the subject’s head rotates relative to the magnetic field. The effects of these artefacts have generally been ameliorated by removing periods of data during which large artefact voltages appear in the EEG traces. However, even when combined with other standard post-processing methods, this strategy does not remove smaller MAs which can dominate the neuronal signals of interest. A number of methods are therefore being developed to characterise the MA by measuring reference signals and then using these in artefact correction. These methods generally assume that the head and EEG cap, plus any attached sensors, form a rigid body which can be characterised by a standard set of six motion parameters. Here we investigate the motion of the head/EEG cap system to provide a better understanding of MAs. We focus on the reference layer artefact subtraction (RLAS) approach, as this allows measurement of a separate reference signal for each electrode that is being used to measure brain activity. Through a series of experiments on phantoms and subjects, we find that movement of the EEG cap relative to the phantom and skin on the forehead is relatively small and that this non-rigid body movement does not appear to cause considerable discrepancy in artefacts between the scalp and reference signals. However, differences in the amplitude of these signals is observed which may be due to differences in geometry of the system from which the reference signals are measured compared with the brain signals. In addition, we find that there is non-rigid body movement of the skull and skin which produces an additional MA component for a head shake, which is not present for a head nod. This results in a large discrepancy in the amplitude and temporal profile of the MA measured on the scalp and reference layer, reducing the efficacy of MA correction based on the reference signals. Together our data suggest that the efficacy of the correction of MA using any reference-based system is likely to differ for different types of head movement with head shake being the hardest to correct. This provides new information to inform the development of hardware and post-processing methods for removing MAs from EEG data acquired simultaneously with fMRI data

Surround-Screen Mobile based Projection: Design and Implementation of Mobile Cave Virtual Reality

Virtual Reality (VR) is the employment of computer devices to simulate real or imaginary environments. Conventional user interfaces stresses the usage of screen displays to interact with the developed environment. The prospect of VR enables the user to be virtually inside the environment. CAVE is one kind of room-shaped VR technology that displays the environment on its walls. We observe some common limitations in the existing CAVE technology, such as fixed space requirements and the difficulty in moving it. In the current work, we present a novel mobile-CAVE system that uses light-weighted materials and portable powered devices. It solves the limitation of re-using the allocated space by packing it and the ability to move it easily. We have assessed our technology by performing usability analysis, power consumption, and mobility experimental study. The profound experiments demonstrated the efficiency of the proposed technology in comparison with former CAVE system. OAPAScopu

Targeting SARS-CoV2 Spike Protein Receptor Binding Domain by Therapeutic Antibodies

The Authors As the number of people infected with the newly identified 2019 novel coronavirus (SARS-CoV2) is continuously increasing every day, development of potential therapeutic platforms is vital. Based on the comparatively high similarity of receptor-binding domain (RBD) in SARS-CoV2 and SARS-CoV, it seems crucial to assay the cross-reactivity of anti-SARS-CoV monoclonal antibodies (mAbs) with SARS-CoV2 spike (S)-protein. Indeed, developing mAbs targeting SARS-CoV2 S-protein RBD could show novel applications for rapid and sensitive development of potential epitope-specific vaccines (ESV). Herein, we present an overview on the discovery of new CoV followed by some explanation on the SARS-CoV2 S-protein RBD site. Furthermore, we surveyed the novel therapeutic mAbs for targeting S-protein RBD such as S230, 80R, F26G18, F26G19, CR3014, CR3022, M396, and S230.15. Afterwards, the mechanism of interaction of RBD and different mAbs were explained and it was suggested that one of the SARS-CoV-specific human mAbs, namely CR3022, could show the highest binding affinity with SARS-CoV2 S-protein RBD. Finally, some ongoing challenges and future prospects for rapid and sensitive advancement of therapeutic mAbs targeting S-protein RBD were discussed. In conclusion, it may be proposed that this review may pave the way for recognition of RBD and different mAbs to develop potential therapeutic ESV

Photo-Voltaic (PV) Monitoring System, Performance Analysis and Power Prediction Models in Doha, Qatar

This study aims developing customized novel data acquisition for photovoltaic systems under extreme climates by utilizing off-the-shelf components and enhanced with data analytics for performance evaluation and prediction. Microcontrollers and sensors are used to measure meteorological and electrical parameters. Customized signal conditioning, which can withstand high-temperature along with microcontrollers’ development boards enhanced with appropriate interfacing shields and wireless data transmission to iCloud IoT platforms, is developed. In addition, an automatically controllable in-house electronic load of the PV system was developed to measure the maximum power possible from the system. LabVIEW™ program was used to allow ubiquitous access and processing of the recorded data over the used IoT. Furthermore, machine learning algorithms are utilized to predict the PV output power by utilizing data collected over a two-year span. The result of this study is the commissioning of original hardware for PV study under extreme climates. This study also shows how the use of specific ML algorithms such as Artificial Neural Network (ANN) can successfully provide accurate predictions with low root-mean-squared error (RMSE) between the predicted and actual power. The results support reliable integration of PV systems into smart-grids for efficient energy planning and management, especially for arid and semi-arid regions

Machine Learning in Wearable Biomedical Systems

Wearable technology has added a whole new dimension in the healthcare system by real-time continuous monitoring of human body physiology. They are used in daily activities and fitness monitoring and have even penetrated in monitoring the health condition of patients suffering from chronic illnesses. There are a lot of research and development activities being pursued to develop more innovative and reliable wearable. This chapter will cover discussions on the design and implementation of wearable devices for different applications such as real-time detection of heart attack, abnormal heart sound, blood pressure monitoring, gait analysis for diabetic foot monitoring. This chapter will also cover how the signals acquired from these prototypes can be used for training machine learning (ML) algorithm to diagnose the condition of the person wearing the device. This chapter discusses the steps involved in (i) hardware design including sensors selection, characterization, signal acquisition, and communication to decision-making subsystem and (ii) the ML algorithm design including feature extraction, feature reduction, training, and testing. This chapter will use the case study of the design of smart insole for diabetic foot monitoring, wearable real-time heart attack detection, and smart-digital stethoscope system to show the steps involved in the development of wearable biomedical systems

Wearable real-time heart attack detection and warning system to reduce road accidents

Heart attack is one of the leading causes of human death worldwide. Every year, about 610,000 people die of heart attack in the United States alone—that is one in every four deaths—but there are well understood early symptoms of heart attack that could be used to greatly help in saving many lives and minimizing damages by detecting and reporting at an early stage. On the other hand, every year, about 2.35 million people get injured or disabled from road accidents. Unexpectedly, many of these fatal accidents happen due to the heart attack of drivers that leads to the loss of control of the vehicle. The current work proposes the development of a wearable system for real-time detection and warning of heart attacks in drivers, which could be enormously helpful in reducing road accidents. The system consists of two subsystems that communicate wirelessly using Bluetooth technology, namely, a wearable sensor subsystem and an intelligent heart attack detection and warning subsystem. The sensor subsystem records the electrical activity of the heart from the chest area to produce electrocardiogram (ECG) trace and send that to the other portable decision-making subsystem where the symptoms of heart attack are detected. We evaluated the performance of dry electrodes and different electrode configurations and measured overall power consumption of the system. Linear classification and several machine algorithms were trained and tested for real-time application. It was observed that the linear classification algorithm was not able to detect heart attack in noisy data, whereas the support vector machine (SVM) algorithm with polynomial kernel with extended time–frequency features using extended modified B-distribution (EMBD) showed highest accuracy and was able to detect 97.4% and 96.3% of ST-elevation myocardial infarction (STEMI) and non-ST-elevation MI (NSTEMI), respectively. The proposed system can therefore help in reducing the loss of lives from the growing number of road accidents all over the worldAcknowledgments: The publication of this article was funded by the Qatar National Library. This work was supported in part by the Undergraduate Research Experience Program (UREP) under Grant number UREP19-069-2-031, in part by the Qatar University Student Grant under Grant number QUST-CENG-SPR\2017-23.Scopu