Identification of moving limb using near infrared spectroscopic signals for brain activation

A method is described for classifying near-infrared spectroscopy (NIRS) signals measured for motor imagery and/or execution using the left or right hand. The measurement time intervals and the signal channels are used as features. The signals are discriminated using a support vector machine. Experiments demonstrated that this method has a higher generalization capability than a previous method for classifying NIRS signals. Testing of its ability to classify the signals according to whether they are for right- or left-hand motor imagery and/or movement demonstrated that its classification of NIRS signals satisfies the two-category classification problem. A promising application is to brain-computer interfaces, a potential communication tool for paralyzed individuals.