Frontiers Conferences

Neural prostheses, or brain-computer interfaces (BCIs), have the potential to substantially increase quality of life for people suffering from motor disorders, including paralysis and amputation. These systems translate recorded neural signals into control signals that guide a paralyzed arm, artificial limb, or computer cursor. Although current laboratory demonstrations provide a compelling proof-ofconcept, the field must continue to increase performance to achieve clinical viability. Many BCIs use activity from motor and/or premotor cortex to achieve continuous control. These BCIs can be viewed from a feedback control perspective, as the motor field has done for the native limb: the brain is the controller of a new plant, defined by the BCI. This perspective leads us to two advances that result in significant qualitative and quantitative performance improvements. We tested these advances in closed loop with one rhesus macaque trained in a virtual 3D workspace. On each trial he used a cursor, controlled by the native contralateral limb or a BCI, to acquire a target on a 2D plane within an allotted time period. Neural data were recorded from a 96-electrode array (Blackrock) implanted spanning PMd and M1. Our designs are informed by a feedback model, which assumes the user develops a volitional control signal to achieve a goal given the current state of the world. This signal and task-unconstrained signals (such as sensory feedback, attention) give rise to neural firing, which we record. Finally, the decoding algorithm estimates desired cursor movements from the neural firing, and updates the workspace. By applying the assumptions of this simple feedback model, we augment a basic position/velocity Kalman filter. We consider the position/velocity Kalman filter to represent "baseline" as it meshes with the performance of and is algorithmically similar to methods common in the literature (e.g., Kim et al., 2008). All experiments used spike counts generated by a threshold detector without spike sorting. Such a system has clinical appeal, particularly for arrays with potentially decreased SNR (these experiments were 22-24 months post implantation). Design iterations were tested within the same experimental session using a blocked "ABA" design. Through this design process, we made two advances that substantially improve performance. First, using a standard Kalman filter, we fit neural data to a guess of the desired volitional control signal, instead of observed or instructed kinematics. Second, we developed a modified velocity-only Kalman filter, whose observation model incorporates cursor position as feedback. The new BCI appears more controllable and produces straighter reaches and crisper stops. Compared to the standard Kalman BCI, mean time to target is reduced by nearly a factor of two. This system can run freely for hundreds to thousands of trials, making point-toAdditional Materials