Abstract
The movement of human hands is originated from thought expressed via brain activation. For a handicap person, if his or her brain activation signals are captured and encoded, he or she could grab things by robot hands. Recently, for Brain Computer Interface (BCI), several technologies have been developed including EEG and fNIRS. fNIRS measures changes of oxygen called BOLD signal during activation of brain. In practice it is difficult to sense the BOLD signal stably because the signal continuously changes day by day and depends on individuals. This paper aims at developing a smart calibration system that calibrates parameters at high speed and keeps the generalization ability of the classifier. Using SVM we develop a discriminator that detects starting and ending of the BOLD signal. From this signal the discriminator estimates intended movement of the left and right hand. Because SVM is fast, the system can derive the subject-specific parameters in high speed. For calibration, we use the Differential Evolution method (DE). Trial experiments use the tapping task test and take the BOLD signal data in the motor cortex area. This paper also shows the calibration result and its possible parameters.
