title: Hybrid decoding of both spikes and low-frequency local field potentials for brain-machine interfaces.
abstract: The best-performing brain-machine interfaces (BMIs) to date decode movement intention from intracortically recorded spikes, but these signals may be lost over time. A way to increase the useful lifespan of BMIs is to make more comprehensive use of available neural signals. Recent studies have demonstrated that the local field potential (LFP), a potentially more robust signal, can also be used to control a BMI. However, LFP-driven performance has fallen short of the best spikes-driven performance. Here we report a biomimetic BMI driven by low-frequency LFP that enabled a rhesus monkey to acquire and hold randomly placed targets with 99% success rate. Although LFP-driven performance was still worse than when decoding spikes, to the best of our knowledge this represents the highest-performing LFP-based BMI. We also demonstrate a new hybrid BMI that decodes cursor velocity using both spikes and LFP. This hybrid decoder improved performance over spikes-only decoding. Our results suggest that LFP can complement spikes when spikes are available or provide an alternative control signal if spikes are absent.
authors.forename: Sergey D
authors.initials: SD
authors.lastname: Stavisky
authors.forename: Jonathan C
authors.initials: JC
authors.lastname: Kao
authors.forename: Paul
authors.initials: P
authors.lastname: Nuyujukian
authors.forename: Stephen I
authors.initials: SI
authors.lastname: Ryu
authors.forename: Krishna V
authors.initials: KV
authors.lastname: Shenoy
mesh_terms: D000818:Animals; D062207:Brain-Computer Interfaces; D004566:Electrodes; D019054:Evoked Potentials, Motor; D008253:Macaca mulatta; D009044:Motor Cortex / Q000502:physiology; D009068:Movement / Q000502:physiology