Towards real-time, continuous decoding of gripping force from deep brain local field potentials

Lack of force information and longevity issues are impediments to the successful translation of Brain Computer Interface (BCI) systems for prosthetic control from experimental settings to widespread clinical application. The ability to decode force using Deep Brain Stimulation (DBS) electrodes in the Subthalamic Nucleus (STN) of the Basal Ganglia provides an opportunity to address these limitations. This work explores the use of various classes of algorithms (Wiener filter, Wiener-Cascade model, Kalman filter and Dynamic Neural Networks) and recommends the use of a Wiener-Cascade model for decoding force from STN. This recommendation is influenced by a combination of accuracy and practical considerations to enable real-time, continuous operation. This study demonstrates an ability to decode a continuous signal (force) from the STN in real-time allowing the possibility of decoding more than two states from the brain at low-latenc