Prediction of motor and somatosensory function from human ECoG

Abstract

One of the most challenging issues in recent BCI research is not only achieving high performance, but also creating a sense of ownership of artificial devices. To investigate this issue, sensory-motor integrated BMI system should be considered. In this study, we attempted to predict the somatosensory property of tactile stimulus as well as the movement trajectory and type using elctrocorticography (ECoG) signals. We showed that 1) single-trial 3-D movement trajectory can be estimated from low-frequency ECoG signals with relatively high performance, 2) high-gamma activity can be a robust feature for movement type classification, and 3) the location of pressure stimulation can be classified by macro ECoG signals from sensory-related cortical areas. These results might be applied to the closed-loop BMBI systems which simultaneously encode sensory information during movement decoding.