Biopotential signals are mainly characterized by low amplitude and thus often distorted by extraneous interferences, such as power line interference in the recording environment and movement artifacts during the acquisition process. With the presence of such large-amplitude interferences, subsequent processing and analysis of the acquired signals becomes quite a challenging task that has been reported by many previous studies. A number of software-based filtering techniques have been proposed, with most of them being able to minimize the interferences but at the expense of distorting the useful components of the target signal. Therefore, this study proposes a hardware-based method that utilizes a shielded drive circuit to eliminate extraneous interferences on biopotential signal recordings, while also preserving all useful components of the target signal. The performance of the proposed method was evaluated by comparing the results with conventional hardware and software filtering methods in three different biopotential signal recording experiments (electrocardiogram (ECG), electro-oculogram (EOG), and electromyography (EMG)) on an ADS1299EEG-FE platform. The results showed that the proposed method could effectively suppress power line interference as well as its harmonic components, and it could also significantly eliminate the influence of unwanted electrode lead jitter interference. Findings from this study suggest that the proposed method may provide potential insight into high quality acquisition of different biopotential signals to greatly ease subsequent processing in various biomedical applications.

Since late 2019, the coronavirus disease 2019 (COVID-19) outbreak, caused by SARS-CoV-2, has rapidly evolved to become a global pandemic. Each country was affected but with a varying number of infected cases and mortality rates. Africa was hit late by the pandemic but the number of cases rose sharply. In this study, we investigated 224 SARS-CoV-2 genome sequences from the Global Initiative on Sharing Avian Influenza Data (GISAID) in the early part of the outbreak, of which 69 were from Africa. We analyzed a total of 550 mutations by comparing them with the reference SARS-CoV-2 sequence from Wuhan. We classified the mutations observed based on country and region, and afterwards analyzed common and unique mutations on the African continent as a whole. Correlation analyses showed that the duo variants ORF1ab/RdRp 4715L and S protein 614G variants, which are strongly linked to fatality rate, were not significantly and positively correlated with fatality rates (r = -0.03757, P = 0.5331 and r = -0.2876, P = 0.6389, respectively), although increased number of cases correlated with number of deaths (r = 0.997, P = 0.0002). Furthermore, most cases in Africa were mainly imported from American and European countries, except one isolate with no mutation and was similar to the original isolate from Wuhan. Moreover, unique mutations specific to countries were identified in the early phase of the outbreak but these mutations were not regional-specific. There were common mutations in all isolates across the continent as well as similar isolate-specific mutations in different regions. Our findings suggest that mutation is rapid in SARS-CoV-2 in Africa and although these mutations spread across the continent, the duo variants could not possibly be the sole cause of COVID-19 deaths in Africa in the early phase of the outbreak.

Recently, an EEG-based robotic arm control has offered a key solution to the problem of high level amputation or severe neuromuscular damage. Several attempt to use EMG-based pattern recognition (PR) failed due to insufficient myoelectric signals from the residual limb to perform control functions. The EEG activity recorded from human scalp is used to control the movement of a robotic arm. This can either be achieved when the arm is attached to or separated from the amputee stump by interfacing the brain directly to the robotic arm through the brain-machine interface (BMI). To build an intelligent robotic (or prosthetics) system that will manipulate object seamlessly with multiple degrees of freedom (DoF), it is required that a robust learning algorithm which is able to control the prosthetic arm while interacting with the environment should be implemented. However, the conventional machine learning approach of using handcrafted features to design a robot controller that can perform multiple task is not a feasible option. Hence, we propose a robust learning control which is based on unsupervised learning algorithm of deep autoencoder. We applied stacked autoencoder to generate our features, and softmax layer is then used to classify five different motor imagery tasks. The proposed method produced an overall accuracy of 98.9% across the four amputees recruited for the experiments. Our algorithm shows a better performance when compared with the state-of-the art classifiers. Thus, the proposed results demonstrates the possibility of providing better control performance for EEG-based prosthesis.