Decoding movement information from cortical activity for invasive BMIs

Abstract

Most invasive brain-machine interfaces (BMIs) have relied on the movement-related information in the firing activities of a number of cortical neurons. Recently, many efforts have been made to represent high-dimensional firing activities of a neuronal ensemble in a low-dimensional features space, visualizing the trajectory of temporal evolution of neural activities. The resulting neural trajectory often provides a sound means to visualize encoding of movement information in neuronal ensembles as well as to improve decoding performance by eliminating noise from irrelevant neurons. The present study aims to build the neural trajectory from motor cortical neurons in a primate performing a center-out task. The neural trajectory built by the standard principal component analysis method well represented hand speed profiles and provided proper feature vectors to a subsequent decoding algorithm. The results suggest an effective way of single-trial speed decoding for invasive BMIs.