01419nas a2200181   4500008004100000022001400041245011600055210006900171260001200240300001000252490000600262520084000268653001601108100001801124700002601142700002101168856004801189       2010                        eng d  a1741-255200aA comparison of regression techniques for a two-dimensional sensorimotor rhythm-based brain-computer interface.0 acomparison of regression techniques for a twodimensional sensori  c02/2010  a160030 v73 aPeople 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.10aYoung Adult1 aFruitet, Joan1 aMcFarland, Dennis, J.1 aWolpaw, Jonathan  uhttp://www.ncbi.nlm.nih.gov/pubmed/20075503