PR-SVM algorithm for recognition of human hand tapping using functional near infrared spectroscopy

Abstract

Researches of human Brain Computer Interface (BCI) for the objective of diagnosis and rehabilitation have been recently increased. Cerebral oxygenation and blood flow on particular regions of human brain can be measured using a non-invasive technique – fNIRS (functional Near Infrared Spectroscopy). In this paper, a study of recognition algorithm will be described for recognizing whether one taps his/her left hand or right hand. Data with noises and artifacts collected from a multi-channel system will be pre-processed using a Savitzky- Golay filter for getting more smoothly fNIRS data. Characteristics of the filtered signals during left and right hand tapping process will be extracted using a Polynomial Regression (PR)-Support Vector Machine (SVM) algorithm. Coefficients of the polynomial determined by the PR algorithm, which correspond to Oxygen-Hemoglobin (Oxy- Hb) concentration changes, will be applied for the recognition of hand tapping. Then the SVM will be employed to validate the obtained coefficient data for the hand tapping recognition. Experimental results have been done many trials on 3 subjects to illustrate the effectiveness of the proposed method.