Identification of fNIRS based Brain Activity using Adaptive Algorithm

Abstract

Functional near infrared spectroscopy (fNIRS) is non-invasive brain imaging techniques that detects the cortical activity by measuring the change in the concentration of oxy-hemoglobin and de-oxy hemoglobin. It uses near infrared light of two wave lengths, 760 nm and 830 nm. NIRS is emerging neuro imaging modality with high temporal resolution. The advantage of NIRS system over other neuro imaging modalities is low cost, portable, safe and somehow results in short period of time. The scalp remains intact throughout the experiment. In this study we present a method for identification of brain activity by using fNIRS data. The general linear model has been used in study with predicted blood oxygen level dependent (BOLD) response signal and its delayed versions. The normalized least mean square (NLMS) algorithm has been used for identification of unknown parameters in the model recursively. A one way t-test has been performed for the significance of results.