title: Neural signal processing and closed-loop control algorithm design for an implanted neural recording and stimulation system.
abstract: A fully autonomous intracranial device is built to continually record neural activities in different parts of the brain, process these sampled signals, decode features that correlate to behaviors and neuropsychiatric states, and use these features to deliver brain stimulation in a closed-loop fashion. In this paper, we describe the sampling and stimulation aspects of such a device. We first describe the signal processing algorithms of two unsupervised spike sorting methods. Next, we describe the LFP time-frequency analysis and feature derivation from the two spike sorting methods. Spike sorting includes a novel approach to constructing a dictionary learning algorithm in a Compressed Sensing (CS) framework. We present a joint prediction scheme to determine the class of neural spikes in the dictionary learning framework; and, the second approach is a modified OSort algorithm which is implemented in a distributed system optimized for power efficiency. Furthermore, sorted spikes and time-frequency analysis of LFP signals can be used to generate derived features (including cross-frequency coupling, spike-field coupling). We then show how these derived features can be used in the design and development of novel decode and closed-loop control algorithms that are optimized to apply deep brain stimulation based on a patient's neuropsychiatric state. For the control algorithm, we define the state vector as representative of a patient's impulsivity, avoidance, inhibition, etc. Controller parameters are optimized to apply stimulation based on the state vector's current state as well as its historical values. The overall algorithm and software design for our implantable neural recording and stimulation system uses an innovative, adaptable, and reprogrammable architecture that enables advancement of the state-of-the-art in closed-loop neural control while also meeting the challenges of system power constraints and concurrent development with ongoing scientific research designed to define brain network connectivity and neural network dynamics that vary at the individual patient level and vary over time.
authors.forename: Lei
authors.initials: L
authors.lastname: Hamilton
authors.forename: Marc
authors.initials: M
authors.lastname: McConley
authors.forename: Kai
authors.initials: K
authors.lastname: Angermueller
authors.forename: David
authors.initials: D
authors.lastname: Goldberg
authors.forename: Massimiliano
authors.initials: M
authors.lastname: Corba
authors.forename: Louis
authors.initials: L
authors.lastname: Kim
authors.forename: James
authors.initials: J
authors.lastname: Moran
authors.forename: Philip D
authors.initials: PD
authors.lastname: Parks
authors.lastname: Sang Chin
authors.forename: Alik S
authors.initials: AS
authors.lastname: Widge
authors.forename: Darin D
authors.initials: DD
authors.lastname: Dougherty
authors.forename: Emad N
authors.initials: EN
authors.lastname: Eskandar
mesh_terms: D000465:Algorithms; D001921:Brain / Q000502:physiology; D046690:Deep Brain Stimulation / Q000295:instrumentation* / Q000379:methods; D006801:Humans; D058542:Implantable Neurostimulators; D012815:Signal Processing, Computer-Assisted / Q000295:instrumentation; D012984:Software