Cognitive Control Signals for Neural Prosthetics

Abstract

Single cell and multiunit signals were recorded by a multichannel recording system (Plexon Inc, Texas) from 96 paralyne coated tungsten or platinum/iridium electrodes (impedance ≈ 300 kΩ) (Microprobe Inc. Maryland) implanted in the medial intraparietal area (MIP), a subdivision of the parietal reach region (PRR), and area 5 (1) of three rhesus monkeys trained to perform a memory reach task. One monkey (monkey S) also had 64 electrodes implanted in the dorsal premotor area (PMd) in a separate surgery. Each session consisted of a reach segment and a brain control segment. Trials in both segments were initiated in the same way: after the monkeys acquired a central red fixation point with the eyes and touched a central green target, a peripheral cue was flashed indicating the location of one out of four, five, six, or eight reach targets ( Figure 1a) (cue epoch). Reach targets were uniformly distributed around the central fixation point. As soon as the fixation point and central green target were acquired, hand and eye movements were restricted by a real time behavioural controller (LabVIEW, National Instruments). Eye position was monitored using a scleral search coil (CNC Engineering, monkeys S and O), or an infrared reflection system (ISCAN, monkey C) while hand position was monitored using an acoustic touch screen (ELO Touch). In order to successfully complete a trial, the monkeys were not allowed to move their eyes. In addition, the reaching hand had to be in contact with the centrally located green target at all times except after the GO signal which appeared during the reach segment of the session. After the offset of the cue, a delay of 1.5 ± 0.3 seconds ensued. During the reach segment, the green central target was extinguished after the memory period

Dissociable effects of the implicit and explicit memory systems on learning control of reaching

“…For a given spatial distance between two movements, increasing the sensitivity of the bases to changes in arm position is equivalent to increasing the separation distance between two movements, thus resulting in improved learning. Attention or other cues can indeed modulate tuning of neurons (Reynolds et al, 2000;Moran and Desimone, 1985;Musallam et al, 2004). For example, visual stimuli with low luminance contrast elicit higher activation of V4 neurons when attention is directed to the stimulus location than when attention is directed away from the stimulus location, demonstrating that attention increases the sensitivity to luminance contrast (Reynolds et al, 2000).…”

Free-paced high-performance brain–computer interfaces

“…Several groups have recently demonstrated that monkeys (Serruya et al 2002, Taylor et al 2002, Carmena et al 2003, Musallam et al 2004 and humans (Kennedy and Bakay 1998, Kennedy et al 2000, Hochberg et al 2006 can learn to move computer cursors and robotic arms to various target locations simply by activating neural populations that participate in natural arm movements. But even these compelling proof-of-concept laboratory demonstration systems fall short of exhibiting the full level of control needed for many everyday behaviors.…”

“…Several cortical areas in rhesus monkeys, and in homologous areas in humans, participate in the preparation and execution of natural arm movements. Areas include the medial intraparietal area (MIP)/parietal reach region (PRR) with largely plan activity prior to the movement onset , Musallam et al 2004, the dorsal aspect of premotor cortex (PMd) with both plan and movement activities , and motor cortex (M1) with largely movement activity around the time of the movement (Taylor et al 2002, Serruya et al 2002, Carmena et al 2003, Hochberg et al 2006. Neural prostheses measure electrical neural signals (action and local field potentials) using arrays of chronically-implanted electrodes, extract action potential times for each neuron on every electrode and, finally, estimate the desired arm movement and generate control signals for guiding prosthetic devices.…”

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).…”