Direct Cortical Control of 3D Neuroprosthetic Devices

Abstract

Three-dimensional (3D) movement of neuroprosthetic devices can be controlled by the activity of cortical neurons when appropriate algorithms are used to decode intended movement in real time. Previous studies assumed that neurons maintain fixed tuning properties, and the studies used subjects who were unaware of the movements predicted by their recorded units. In this study, subjects had real-time visual feedback of their brain-controlled trajectories. Cell tuning properties changed when used for brain-controlled movements. By using control algorithms that track these changes, subjects made long sequences of 3D movements using far fewer cortical units than expected. Daily practice improved movement accuracy and the directional tuning of these units.

A mixed filter algorithm for cognitive state estimation from simultaneously recorded continuous and binary measures of performance

“…state-space estimation procedures, by extending the current algorithm to construct mixed filter algorithms for continuous observations and point processes (continuous time binary processes) in either discrete or continuous time (Eden et al 2004;Snyder and Miller 1991). Second, the mixed filter algorithm may make it possible to use simultaneously recorded ensemble neural spiking activity and local field potentials to control neural prosthetic devices and brain machine interfaces (Musallam et al 2004;Serruya et al 2002;Taylor et al 2002;Wessberg et al 2005). Finally, the mixed filter algorithms may also suggest a new approach to analyzing cardiovascular and autonomic control from simultaneously recorded cardiovascular, respiratory and R − R interval measurements (Barbieri et al 1996(Barbieri et al , 1997(Barbieri et al , 2002 as well as for studying the dynamics of seismic events (Granat et al 2003).…”

Techniques for extracting single-trial activity patterns from large-scale neural recordings

“…An implicit form of dimensionality reduction is often performed in the context of neural prosthetic systems, when the trajectory of the arm is 'decoded' from simultaneously-recorded neurons [62][63][64]. High (~100) dimensional neural data is collapsed into a low (e.g., 3) dimensional arm trajectory estimate.…”

“…The decoded trajectory is thus a concise 'explanation' or summary of the high-dimensional neural data. Decoding techniques include linear filters [63,64], the population vector [62,65,66], and recursive Bayesian decoding using state-space models [67][68][69]. Most of these approaches attempt to infer something that can be directly observed/inferred on most trials (e.g., actual or expected arm trajectory), yet in some ways this is an advantage, as it allows evaluation of the performance of different decoding techniques.…”

Brain-Computer Interfaces in Medicine

“…12,16 Ongoing studies in a number of laboratories are working toward achieving natural control of devices such as a prosthetic arm using electrode microarrays implanted in the motor cortex or other cortical areas of nonhuman primates. [109][110][111][112][113][114] Plans are under way in several centers to translate these studies into human trials.…”

“…The acquisition and maintenance of BCI-based skills like reliable multidimensional movement control require comparable plasticity (eg, as described by various investigators 10,24,110,113 ). Brain-computer interface operation rests on the effective interaction of 2 adaptive controllers, the CNS and the BCI.…”