Novel Feature Generation for Classification of Motor Activity from Functional Near-Infrared Spectroscopy Signals Using Machine Learning.

Recent research in the field of cognitive motor action decoding focuses on data acquired from Functional Near-Infrared Spectroscopy (fNIRS) and its analysis. This research aims to classify two different motor activities, namely, mental drawing (MD) and spatial navigation (SN), using fNIRS data from non-motor baseline data and other motor activities. Accurate activity detection in non-stationary signals like fNIRS is challenging and requires complex feature descriptors.

Functional near infrared spectroscopy for brain functional connectivity analysis: A graph theoretic approach.

Background: Functional Near-Infrared Spectroscopy is an optical brain monitoring technique which uses NIRS to perform functional neuroimaging. It uses near-infrared light for measuring brain activity and to estimate the cortical hemodynamic activity in the brain due to motor activity. Functional NIRS measures the changes in oxygen levels in oxygenated and deoxygenated hemoglobin by optical absorption.

Age related changes in lipid peroxidation and antioxidants in elderly people.

Advanced age is associated with an accumulation of free radical damage, which leads to physiological and clinical modifications. Age related changes resulting from free radical reactions include increasing levels of lipid peroxides, alterations in enzyme activities and greater osmotic fragility. The present study was conducted to estimate the level of lipid peroxidation product-Malondialdehyde and antioxidants Catalase and Glutathione in elderly people.