During recent decades, stress-related neuropsychiatric disorders such as anxiety, depression, chronic tension headache, and migraine have established their stronghold in the lives of a vast number of people worldwide. In order to address this global phenomenon, intensive studies have been carried out leading to the advancement of drugs like anti-depressants, anxiolytics, and analgesics which although help in combating the symptoms of such disorders but also create long-term side effects. Thus, as an alternative to such clinical practices, various complementary therapies such as yoga and meditation have been proved to be effective in alleviating the causes and symptoms of different neuropsychiatric disorders. The role of altered brain waves in this context has been recognized and needs to be pursued at the highest level. Thus, the current study provides a review focused on describing the effects of yoga and meditation on anxiety and depression as well as exploring brain waves as a tool for assessing the potential of these complementary therapies for such disorders.

Background: Reliability and validity of features are important statistical concepts used by researchers to evaluate the quality of a research and draw meaningful conclusions from a study. One of the most popular ways of evaluating the reliability is by quantifying using intra-class correlation coefficient (ICC). We aim to develop a user-friendly, interactive web application that enables scholars and researchers to visualize the reliability of quantitative EEG (qEEG) features across various subjects. To our knowledge, currently there are no applications that provide a platform to visualize the reliability of features. Results: We designed a web application qEEGViz — a freely accessible, easy-to-use, open source web-application that can be used by researchers and scholars to interactively visualize the reliability of quantitative EEG (qEEG) features in the form of topographic maps across various subjects, electrode references and epochs lengths. It is a democratic platform which gives the user flexibility to upload raw EEG signals and adjust parameters like power features, electrode reference and epoch lengths according to their requirements. Finally, the web application outputs the visualization of reliability of the qEEG features in the form of topographic map. Conclusion: This tool is an academic contribution created solely for scholars and researchers to enhance the statistical power of the EEG experiment to detect a true treatment effect. It is a non-profitable open source web-application, independent of any operating system and can easily run on the local server. The source code and step by step instructions are provided on: https://github.com/akarshijain/qeegviz.