Simultaneous Classification of Both Mental Workload and Stress Level Suitable for an Online Passive Brain–Computer Interface

Research studies on EEG-based mental workload detection for a passive BCI generally focus on classifying cognitive states associated with the performance of tasks at different levels of difficulty, with no other aspects of the user’s mental state considered. However, in real-life situations, different aspects of the user’s state such as their cognitive (e.g., level of mental workload) and affective (e.g., level of stress/anxiety) states will often change simultaneously, and performance of a BCI system designed considering just one state may be unreliable. Moreover, multiple mental states may be relevant to the purposes of the BCI—for example both mental workload and stress level might be related to an aircraft pilot’s risk of error—and the simultaneous prediction of states may be critical in maximizing the practical effectiveness of real-life online BCI systems. In this study we investigated the feasibility of performing simultaneous classification of mental workload and stress level in an online passive BCI. We investigated both subject-specific and cross-subject classification approaches, the latter with and without the application of a transfer learning technique to align the distributions of data from the training and test subjects. Using cross-subject classification with transfer learning in a simulated online analysis, we obtained accuracies of 77.5 ± 6.9% and 84.1 ± 5.9%, across 18 participants for mental workload and stress level detection, respectively.

Simultaneous prediction of functionally dependent random variables by maximum likelihood estimation

The paper presents a fundamental parametric approach to simultaneous forecasting of a vector of functionally dependent random variables. The motivation behind the proposed method is the following: each random variable at interest is forecasted by its own model and then adjusted in accordance with the functional link. The method incorporates the assumption that models’ errors are independent or weekly dependent. Proposed adjustment is explicit and extremely easy-to-use. Not only does it allow adjusting point forecasts, but also it is possible to adjust the expected variance of errors, that is useful for computation of confidence intervals. Conducted thorough simulation and empirical testing confirms, that proposed method allows to achieve a steady decrease in the mean-squared forecast error for each of predicted variables.

Combined deep CNN–LSTM network-based multitasking learning architecture for noninvasive continuous blood pressure estimation using difference in ECG-PPG features

The pulse arrival time (PAT), the difference between the R-peak time of electrocardiogram (ECG) signal and the systolic peak of photoplethysmography (PPG) signal, is an indicator that enables noninvasive and continuous blood pressure estimation. However, it is difficult to accurately measure PAT from ECG and PPG signals because they have inconsistent shapes owing to patient-specific physical characteristics, pathological conditions, and movements. Accordingly, complex preprocessing is required to estimate blood pressure based on PAT. In this paper, as an alternative solution, we propose a noninvasive continuous algorithm using the difference between ECG and PPG as a new feature that can include PAT information. The proposed algorithm is a deep CNN–LSTM-based multitasking machine learning model that outputs simultaneous prediction results of systolic (SBP) and diastolic blood pressures (DBP). We used a total of 48 patients on the PhysioNet website by splitting them into 38 patients for training and 10 patients for testing. The prediction accuracies of SBP and DBP were 0.0 ± 1.6 mmHg and 0.2 ± 1.3 mmHg, respectively. Even though the proposed model was assessed with only 10 patients, this result was satisfied with three guidelines, which are the BHS, AAMI, and IEEE standards for blood pressure measurement devices.

Fast Screening of Biomembrane-Permeable Compounds in Herbal Medicines Using Bubble-Generating Magnetic Liposomes Coupled with LC–MS

A novel strategy based on the use of bionic membrane camouflaged magnetic particles and LC–MS was developed to quickly screen the biomembrane-permeable compounds in herbal medicines. The bionic membrane was constructed by bubble-generating magnetic liposomes loaded with NH4HCO3 (BMLs). The lipid bilayer structure of the liposomes enabled BMLs to capture biomembrane-permeable compounds from a herbal extract. The BMLs carrying the compounds were then separated from the extract by a magnetic field. Upon heat treatment, NH4HCO3 rapidly decomposed to form CO2 bubbles within the liposomal bilayer, and the captured compounds were released from BMLs and analyzed by LC–MS. Jinlingzi San (JLZS), which contains various natural ingredients, was chosen to assess the feasibility of the proposed method. As a result, nine potential permeable compounds captured by BMLs were identified for the first time. Moreover, an in vivo animal study found that most of the compounds screened out by the proposed method were absorbed into the blood. The study provides a powerful tool for rapid and simultaneous prediction of multiple biomembrane-permeable components.

FORECAST OF TIME PROBABLE DISEASES IN PERIOD OF EPIDEMICS OF INFLUENZA AND CORONAVIRUS

The purpose of the research is to determine the frequency of infection occurrence and use it for forecasting. According to the dates of human pathologies, the frequency of diseases (PZ) of 2400 parents and children, men and women was determined. To improve the accuracy of determining the PZ, astronomical units were used to record the time of occurrence of pathologies: 4 phases of the moon, 4 components of the moon's declination, and a day. The hereditary periodicity of diseases (NPD) was revealed, which is shown in graphic and digital forms. The refinery is used for predicting the time of probable acute infectious diseases and their complications. The goal of forecasting is to take timely preventive measures. 7 forecasting methods have been developed, 2 computer programs have been created that use different coordinate systems, and more than 1,600 computer forecasts have been made. With an error of + 1-4 days, 50% - 62% of diseases of children, women and men who were ill on average 3 times a year and more often are predicted. In 2014 registered application for invention No. 2014116101 "Method for simultaneous prediction of the time of probable acute infectious diseases of a person and the optimal date of vaccination". Rospatent's conclusion: ".. the invention can be implemented, but its practical applicability has not been confirmed." The implementation of the invention can be carried out in the period of the pandemic coronavirus. The conclusion about the "impracticability of forecasting" hinders the practical applicability of the invention and predicting the time of probable covid-19 diseases.