Resting state EEG assisted imagined vowel phonemes recognition by native and non-native speakers using brain connectivity measures.

Communication is challenging for disabled individuals, but with advancement of brain-computer interface (BCI) systems, alternative communication systems can be developed. Current BCI spellers, such as P300, SSVEP, and MI, have drawbacks like reliance on external stimuli or conversation irrelevant mental tasks. In contrast to these systems, Imagined speech based BCI systems rely on directly decoding the vowels/words user is thinking, making them more intuitive, user friendly and highly popular among Brain-Computer-Interface (BCI) researchers.

Digital Well-being Through the Use of Technology-A Perspective.

- John Donne According to the World Health Organization, health is "a state of complete physical, mental and social well-being and not merely the absence of disease and infirmity." Our healthcare industry, public behaviors, and environment have grown exponentially with digital technologies in the era of the 4 industrial revolution. Due to rapid digitalization and easy availability of the internet, we are now online round the clock on our digital devices, leaving behind digital traces/information.

Evaluation of existing Home Based Newborn Care (HBNC) services and training for improving performance of Accredited Social Health Activists (ASHA) in rural India: A multiple observation study.

Background: One-fourth of global neonatal deaths occur in India alone. Accredited Social Health Activists (ASHA) was launched with the purpose of improving healthcare services, including neonatal survival primarily in rural areas. The aim of this study is to determine the status of ASHA's knowledge, practices, and attitude regarding Home Based Newborn Care (HBNC) services, as well as to provide necessary trainings for improvement of their performance.

SNOAR: a new regression approach for the removal of ocular artifact from multi-channel electroencephalogram signals.

Electroencephalogram (EEG) signals are often corrupted by undesirable sources like electrooculogram (EOG) artifacts, which have a substantial impact on the performance of EEG-based systems. This study proposes a new singular spectrum analysis (SSA)-non-negative matrix factorization (NMF)-based ocular artifact removal (SNOAR) method to suppress ocular artifacts from multi-channel EEG signals. First, SSA was used to estimate EOG artifacts using a small subset of frontal electrodes.

Stratification of rheumatoid arthritis cohort using Ayurveda based deep phenotyping approach identifies novel genes in a GWAS.

Background And Aim: Genome wide association studies have scaled up both in terms of sample size and range of complex disorders investigated, but these have explained relatively little phenotypic variance. Of the several reasons, phenotypic heterogeneity seems to be a likely contributor for missing out genetic associations of large effects. Ayurveda, the traditional Indian system of medicine is one such tool which adopts a holistic deep phenotyping approach and classifies individuals based on their body constitution/prakriti.