Neural prosthetic systems: Current problems and future directions

Abstract

Abstract-By decoding neural activity into useful behavioral commands, neural prosthetic systems seek to improve the lives of severely disabled human patients. Motor decoding algorithms, which map neural spiking data to control parameters of a device such as a prosthetic arm, have received particular attention in the literature. Here, we highlight several outstanding problems that exist in most current approaches to decode algorithm design. These include two problems that we argue will unlikely result in further dramatic increases in performance, specifically spike sorting and spiking models. We also discuss three issues that have been less examined in the literature, and we argue that addressing these issues may result in dramatic future increases in performance. These include: non-stationarity of recorded waveforms, limitations of a linear mappings between neural activity and movement kinematics, and the low signal to noise ratio of the neural data. We demonstrate these problems with data from 39 experimental sessions with a non-human primate performing reaches and with recent literature. In all, this study suggests that research in cortically-controlled prosthetic systems may require reprioritization to achieve performance that is acceptable for a clinically viable human system.

Spatiotemporal Variation of Multiple Neurophysiological Signals in the Primary Motor Cortex during Dexterous Reach-to-Grasp Movements

“…Because previous studies have indicated that multi-unit recordings can provide decoding of direction equivalent to that of single-units (Liu and Newsome, 2006; Chestek et al, 2009), in the present analysis we treated single-unit and multi-unit spike recordings equivalently. Figure 6 illustrates spike recordings from two single units recorded in session Y0225, one from array H (2.0 mm deep to the hemispheric surface) and one from array I (1.5 mm deep).…”

Self-recalibrating classifiers for intracortical brain–computer interfaces

“…A 96-channel silicon electrode array (Blackrock Microsystems, Salt Lake City, UT) was implanted in motor cortex contralateral to the reaching arm. When identifying neural spikes on each electrode, threshold crossings (Chestek et al, 2009; Fraser et al, 2009) were used in place of spike sorting. For monkey L, we also required voltage waveforms to pass a shape heuristic.…”

Decoding 3D reach and grasp from hybrid signals in motor and premotor cortices: spikes, multiunit activity, and local field potentials

“…Spikes may be stable only across days to weeks, and even then only for a subset of channels (Chestek et al 2009;Dickey et al 2009), necessitating the frequent updating of decoding filters (Hochberg et al 2006). On an array where the number of spiking units is small or decreases over time, LF, H1, and H2 signals may provide a substitute signal to create a robust decoder.…”

“…Thus comparisons with MUA-based decoding require a careful analysis of the actual signal employed. The relative performance of decoders based on sorted (our SA signal) or thresholded but unsorted spikes (Chestek et al 2009) is a subject of ongoing research for BCI applications.…”