The goal of this project is to develop statistical models to accurately and efficiently decode population neuronal activity in the motor and premotor cortex. The study focuses on motor behavior as it is easily measured and strongly correlated with neuronal activity. Recent advances in motor cortical brain-machine interfaces have shown that research animals and paralyzed human patients were able to perform rudimentary actions with external devices such as robotic limbs and computer cursors. Neural decoding, which provides control commands to external devices, plays a key role in such interfaces by converting brain signals (e.g., spiking rates of a population of neurons) to kinematic states (e.g., hand position, hand movement direction).
Current decoding models are often based on the strong assumption that the neural signal sequence is a stationary process. This assumption, however, does not take into account the significant dynamic variability of spiking activity over time. Moreover, these methods have either focused on decoding the entire trajectory or on the occurrence times of a few "landmarks" during the movement. Effective coupling of these two complementary strategies can be expected to improve the decoding performance by better exploiting the nature of the landmark-defined movement. This project will develop computational methods to address these two issues. For the non-stationarity, the research team will develop adaptive versions of state-of-the-art decoding methods such as particle filters and point process filters that can capture the varying patterns in neural signals and update the model accordingly. To couple trajectory decoding and time decoding, landmark times will be identified from the neural activity, and then incorporated into the kinematic model. The team will use simultaneous recordings from multi-electrode arrays in the primary motor cortex, the dorsal premotor cortex, and the ventral premotor cortex that were recorded during behavior or visuo-motor tasks. Improved decoding methods are expected to have significant impacts on neural prosthetics.
