Human motor cortex detection using wavelet threshold algorithm and FNIRS technology - Nguyen, Thanh Nghia; Nguyen , Thanh Hai (Journal of Technical Education Science, 2021)
The functional Near-Infrared Spectroscopy (fNIRS) technology has been a noninvasive technique and it has also contracted researchers in studying the brain activity of human in recent years. Human brain research is an essential task for scientists and doctors more understanding about brain activity for diagnosis. In this article, the experiments of lifting her/him left hand up and down were performed to measure the concentration of Oxygenated – Hemoglobin (Oxy-Hb) of the human brain by fNIRS, in which the obtained Oxy-Hb signals measured from the brain have the relationship of human movements. The Oxy-Hb signals were pre-processed using a Savitzky-Golay filter to reduce noise and artifacts and to smooth the fNIRS data. Therefore, a wavelet decomposition algorithm was employed to divide the data into the different components (details – d and approximations – a) for determination of features. Moreover, the components were classified by the mean threshold to determine the motor control area of the human brain, in which the classification of the Oxy-Hb signals may allow to determine the right/left hand lifting. Experimental results were worked out with different subjects to detect the motor area at brain hemisphere related to the right/left hand.