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INNOVATIVE METHODOLOGY

General-Purpose Filter Design for Neural Prosthetic Devices

¹Center for Nervous System Repair, Department of Neurosurgery, Massachusetts General Hospital, Boston; ²Laboratory for Information and Decision Systems, Department of Electrical Engineering and Computer Science and ³Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge; ⁴Harvard/MIT Division of Health Sciences and Technology, Cambridge; ⁵Department of Mathematics and Statistics, Boston University, Boston; and ⁶Neuroscience Statistics Research Laboratory, Department of Anesthesia and Critical Care, Massachusetts General Hospital, Charlestown, Massachusetts

Submitted 20 October 2006; accepted in final form 17 May 2007

Brain-driven 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.

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