Differentiating Mental Stress Levels: Analysing Machine Learning Algorithms Comparatively For EEG-Based Mental Stress Classification Using MNE-Python

Mental stress is a prevalent and consequential condition that impacts individuals' well-being and productivity. Accurate classification of mental stress levels using electroencephalogram (EEG) signals is a promising avenue for early detection and intervention. In this study, we present a comprehensive investigation into mental stress classification using EEG data processed with the MNE-Python library. Our research leverages a diverse set of machines learning algorithms, including Random Forest (RF), Decision Tree, K-Nearest Neighbors (KNN), Multilayer Perceptron (MLP), Support Vector Machine (SVM), Adaboost, and Extreme Gradient Boosting (XGBoost), to discerndifferences in classification performance. We employed a single dataset to ensure consistency in our experiments, facilitating a direct comparison of these algorithms. The EEG data were pre-processed using MNE-Python, which included tasks such as signal cleaning, and feature selection. Subsequently, we applied the selected machine learning models to the processed data and assessed their classification performance in terms of accuracy, precision, recall, and F1-score. Our results demonstrate notable variations in the classification accuracy of mental stress levels across the different algorithms. These findings suggest that the choice of machine learning technique plays a pivotal role in theeffectiveness of EEG-based mental stress classification. Our study not only highlights the potential of MNE-Python for EEG signal processing but also provides valuable insights into the selection of appropriate machine learning algorithms for accurate and reliable mental stress assessment. These outcomes hold promise for the development of robust and practical systems for real-time mental stress monitoring, contributing to enhanced well-being and performance in various domains such as healthcare, education, and workplace environment

Effectiveness of the BBV-152 and AZD1222 vaccines among adult patients hospitalized in tertiary hospitals in Odisha with symptomatic respiratory diseases: A test-negative case–control study

Two vaccines, namely BBV-152 (COVAXIN®) and AZD1222 (COVISHIELD™), were deployed against SARS-CoV-2 in India from January 16, 2021. Frontline health care workers were vaccinated first, followed by the adult population. However, limited data on vaccine effectiveness are available for the population of India. Therefore, we aimed to evaluate the effectiveness of two doses of each of these two common vaccines against COVID-19 infection among hospitalized patients with pulmonary conditions. We adopted a test-negative case–control design and recruited a sample of adults who were admitted to one of six tertiary care hospitals in Odisha. All participants were hospitalized patients with COVID-19-like pulmonary signs and symptoms. Participants who tested positive for SARS CoV-2 via RT-PCR were treated as cases, and those who tested negative were treated as controls. Logistic regression, adjusted for participants' age, sex, and number of comorbidities, was used to calculate the effectiveness of the two vaccines, using the formula: 100*(1 – adjusted odds ratio). Between March and July of 2021, data were collected from 1,614 eligible adults (864 cases and 750 controls). Among all participants, 9.7% had received two doses of one of the two COVID-19 vaccines. Vaccine effectiveness was 74.0% (50.5%−86.0%) for two doses of BBV-152 and 79.0% (65.4%−87.2%) for two doses of AZD1222. Thus, two doses of either BBV-152 or AZD1222 nCoV-19 vaccine were found to be substantially effective in protecting against COVID-19-related infection