Motion Discrimination from EEG Using Logistic Regression and Schmitt-Trigger-Type Threshold

In this study, a robot rehabilitation system is developed for motor paralysis using a brain -- machine interface (BMI) that estimates a patient´s intention of motion from his electroencephalogram (EEG). Then, we consider the forcible movement of the patient´s affected parts by an exoskeleton robot in synchronicity with his estimated intention. Further, we considered a motion discrimination method using an EEG that was measured when a healthy subject executed an upper-arm bending and stretching exercise. As a result, we used the10 -- 14 Hz band overall intensity of an EEG measured at the right parietal region as a feature of motion discrimination and proposed a method that employed logistic regression to obtain the likelihood function of the discriminated motion. Moreover, we used dual threshold processing, well-known as a "Schmitt-trigger gate" in the field of electronic engineering, to calculate the motion discrimination result using the obtained likelihood value. The effectiveness of our proposed method was confirmed through a motion discrimination experiment.