title: Improved linear BMI systems via population averaging.
abstract: We investigate population averaging as a preprocessing stage for linear FIR BMIs. Population averaging is a biologically-inspired technique based on spatial constraints and neuronal correlation. We achieve a statistically significant improvement in accuracy while substantially (45%) reducing model parameters. Further analysis is performed to show that population averaging improves model accuracy by reducing variance in estimating the firing rate from spike bins. However, we find that population averaging provides a greater accuracy improvement than other groupings which also reduce firing rate variance. Our results suggest that appropriate spatial organization of neural signals enhances BMI performance.
authors.affiliation: Dept. of Biomed. Eng., Florida Univ., Gainesville, FL 32611, USA. digiovaj@cnel.ufl.edu
authors.forename: Jack
authors.initials: J
authors.lastname: DiGiovanna
authors.forename: Justin C
authors.initials: JC
authors.lastname: Sanchez
authors.forename: Jose C
authors.initials: JC
authors.lastname: Principe
mesh_terms: D000200:Action Potentials / Q000502:physiology*; D000465:Algorithms; D000818:Animals; D001931:Brain Mapping / Q000379:methods; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D004569:Electroencephalography / Q000379:methods*; D019054:Evoked Potentials, Motor / Q000502:physiology*; D016014:Linear Models; D008959:Models, Neurological; D015233:Models, Statistical; D009044:Motor Cortex / Q000502:physiology*; D051381:Rats; D017207:Rats, Sprague-Dawley; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014584:User-Computer Interface