General-Purpose Filter Design for Neural Prosthetic Devices

Abstract

. Braindriven interfaces depend on estimation procedures to convert neural signals to inputs for prosthetic devices that can assist individuals with severe motor deficits. Previous estimation procedures were developed on an application-specific basis. Here we report a coherent estimation framework that unifies these procedures and motivates new applications of prosthetic devices driven by action potentials, local field potentials (LFPs), electrocorticography (ECoG), electroencephalography (EEG), electromyography (EMG), or optical methods. The brain-driven interface is described as a probabilistic relationship between neural activity and components of a prosthetic device that may take on discrete or continuous values. A new estimation procedure is developed for action potentials, and a corresponding procedure is described for field potentials and optical measurements. We test our framework against dominant approaches in an arm reaching task using simulated traces of ensemble spiking activity from primary motor cortex (MI) and a wheelchair navigation task using simulated traces of EEG-band power. Adaptive filtering is incorporated to demonstrate performance under neuron death and discovery. Finally, we characterize performance under model misspecification using physiologically realistic history dependence in MI spiking. These simulated results predict that the unified framework outperforms previous approaches under various conditions, in the control of position and velocity, based on trajectory and endpoint mean squared errors.

Primary Motor Cortex Tuning to Intended Movement Kinematics in Humans with Tetraplegia

“…We hope to systematically investigate tuning function stability and the effects of learning with more controlled tasks and larger data sets in future studies. Second, it should also be noted that nonstationarity of tuning properties, as well as changes in the recorded neuronal population, can be properly handled in neuroprosthetic applications via intermittent recalibration of decoding filters (e.g., as performed here with filter rebuilding at the beginning of a session) or via continuous tracking of the neuronal population and their tuning properties (Eden et al, 2004a,b;Srinivasan et al, 2007). In addition, as pointed out by Rokni et al (2007), one can also conjecture that an optimal solution manifold might similarly emerge for the neuronal population recorded at the prosthetic interface.…”

Neural prosthetic systems: Current problems and future directions

“…There has been extensive work in modeling spike trains [5], [38]- [42] and estimating firing rates [43]- [47]. While some decode algorithms average over neural activity in small temporal windows [17], some algorithms use firing rates or use spiking models directly [18]. Spiking models are another source of approximation in BMIs.…”

“…In contrast to this shortcoming, models such as the Kalman filter [31], which stipulate a model for physical behavior in arm reaches, have been shown to outperform the linear decoder in a variety of cases [14], [48]. This success led to extensions that assume similar models for physical behavior [5], [13], [17], [18], [21]- [23], [50]. Unfortunately, this class of models for physical behavior is inappropriate in some ways for reaching movements.…”

Likelihood Methods for Point Processes with Refractoriness

“…These techniques have been used for basic neuroscience research (Kass et al, 2011;Okatan et al, 2005;Eldawlatly et al, 2009;Berger et al, 2011;Jenison et al, 2011;So et al, 2012), to improve biophysical neural models (Ahrens et al, 2008;Meng et al, 2011;Mensi et al, 2012), or to design better BMIs (Shoham et al, 2005;Srinivasan et al, , 2007Truccolo et al, 2008;Wang and Principe, 2010;Saleh et al, 2012).…”

“…to help to design algorithms for inferring behaviour/stimulus from previously unseen neural data, a processed referred to as decoding and commonly used in the design of brain-machine interfaces (BMIs) (Shoham et al, 2005;Srinivasan et al, 2007;Sanchez et al, 2008).…”