A comparison of regression techniques for a two-dimensional sensorimotor rhythm-based brain-computer interface.
People can learn to control electroencephalogram (EEG) features consisting of sensorimotor-rhythm amplitudes and use this control to move a cursor in one, two or three dimensions to a target on a video screen. This study evaluated several possible alternative models for translating these EEG features into two-dimensional cursor movement by building an offline simulation using data collected during online performance. In offline comparisons, support-vector regression (SVM) with a radial basis kernel produced somewhat better performance than simple multiple regression, the LASSO or a linear SVM. These results indicate that proper choice of a translation algorithm is an important factor in optimizing brain-computer interface (BCI) performance, and provide new insight into algorithm choice for multidimensional movement control.
Fruitet
Joan
J
Ecole Normale Supérieure, 45 rue d'Ulm, 75230 Paris Cedex 05, France. joan.fruitet@gmail.com
McFarland
Dennis J
DJ
Wolpaw
Jonathan R
JR
D000328:Adult; D000465:Algorithms; D003198:Computer Simulation; D004569:Electroencephalography / Q000379:methods*; D005260:Female; D006801:Humans; D016014:Linear Models; D008297:Male; D008606:Mental Processes / Q000502:physiology*; D012044:Regression Analysis; D012815:Signal Processing, Computer-Assisted; D013003:Somatosensory Cortex / Q000502:physiology*; D013997:Time Factors; D014584:User-Computer Interface; D055815:Young Adult