Offline comparison of spatial filters for two-dimensional movement control with noninvasive field potentials

Abstract

Paralyzed individuals can control the movement of an assistive device using changes in electroencephalographic (EEG) power resulting from attempted movements. Simultaneous, proportional control of two-dimensional (2D) device movements can be achieved with the concurrent modulation of brain activity that is associated with the attempted movement and rest of two independent body parts. Movement control may be improved by spatial filtering methods that recombine raw EEGs to form new signals with more focused information about the underlying brain activity. This study compared spatial filters offline for improving simultaneous proportional 2D movement commands from EEGs. Filtering options evaluated were common average referencing, Laplacian, independent component analysis, principle component analysis, and two novel ways of applying common spatial pattern (CSP) analysis. CSP analysis is a supervised algorithm that optimally recombines EEGs collected under two known conditions. Both CSP options resulted in more accurate movement prediction than the other filtering options. CSP was particularly advantageous when separating EEGs associated with neighboring or overlapping areas on the motor homunculus. Finally, CSP performed well using smaller subsets of filtered signals, thus making CSP practical and efficient for simultaneous 2D control. A 2D online cursor control example using CSP filtering is included to show CSP's utility.