Linear Regression Algorithm for Hand Tapping Recognition Using Functional Near Infrared Spectroscopy
This paper proposed a linear regression (LR) algorithm for hand tapping recognition using functional Near Infrared Spectroscopy (fNIRS). Brain data with noise and artifacts were re-processed to obtain data smoothy using a Savitzky-Golay filter. The smoothy data were calculated using the proposed LR algorithm in order to produce the angular coefficients of the straight lines which correspond to oxygen-Hemoglobin (Oxy-Hb) concentration. Therefore, one can distinguish the right and left hand tapping tasks based on the different angular coefficients of the lines corresponding to the difference of the right and left brain Oxy-Hb. In addition, the difference of the left and right brain activities were determined based on comparing the angular coefficients. Experimental results showed to illustrate the effectiveness of the proposed method.
KeywordsHand tapping linear regression algorithm and left and right hand brain
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