Unscented Kalman Filter for Brain-Machine Interfaces

Abstract

Brain machine interfaces (BMIs) are devices that convert neural signals into commands to directly control artificial actuators, such as limb prostheses. Previous real-time methods applied to decoding behavioral commands from the activity of populations of neurons have generally relied upon linear models of neural tuning and were limited in the way they used the abundant statistical information contained in the movement profiles of motor tasks. Here, we propose an n-th order unscented Kalman filter which implements two key features: (1) use of a non-linear (quadratic) model of neural tuning which describes neural activity significantly better than commonly-used linear tuning models, and (2) augmentation of the movement state variables with a history of n-1 recent states, which improves prediction of the desired command even before incorporating neural activity information and allows the tuning model to capture relationships between neural activity and movement at multiple time offsets simultaneously. This new filter was tested in BMI experiments in which rhesus monkeys used their cortical activity, recorded through chronically implanted multielectrode arrays, to directly control computer cursors. The 10th order unscented Kalman filter outperformed the standard Kalman filter and the Wiener filter in both off-line reconstruction of movement trajectories and real-time, closed-loop BMI operation.

Single-unit activity, threshold crossings, and local field potentials in motor cortex differentially encode reach kinematics

“…Unfortunately, since speed is a nonlinear transform of velocity, this implies that linear decoders such as the Kalman filter (Wu and Hatsopoulos 2008) may not actually be optimal when based on TC or LFP H inputs. However, nonlinear state-space algorithms abound and have proven fruitful in decoding applications (e.g., Koyama et al 2010;Li et al 2009;Brockwell et al 2004;Shpigelman et al 2008;Dethier et al 2013). Another approach to accounting for speed in improving BMI control would be to treat it as a "nuisance variable," and use latent variable approaches to mitigate its effect on decoding (Lawhern et al 2010;Paninski et al 2010).…”

Motor Cortical Correlates of Arm Resting in the Context of a Reaching Task and Implications for Prosthetic Control

“…It has been previously shown that hand velocity can be predicted from motor cortical ensemble activity (Georgopoulos et al, 1988;Schwartz, 1994;Lebedev et al, 2005;Li et al, 2009). To determine whether this model was also applicable during IT periods, we trained an LGF decoder (Koyama et al, 2010a,2010b) using the velocity tuning model (decoder configuration D1, see Materials and Methods).…”

Uso de interfaces cerebro-computador para la decodificación de la cinemática de miembro superior e inferior

“…Antes de comenzar con la decodificación debe elegirse el tipo de modelo que se empleará. Entre las elecciones más usuales destacan los filtros lineales, especialmente el de Wiener [12], [13] y el de Kalman [14], [15].…”