02353nas a2200229   4500008004100000022001400041245008900055210006900144260001200213300001400225490000700239520162900246653003101875653003501906653002701941653001301968100002301981700002602004700002102030700002402051856004802075       2004                        eng d  a1534-432000aConversion of EEG activity into cursor movement by a brain-computer interface (BCI).0 aConversion of EEG activity into cursor movement by a braincomput  c09/2004  a331–3380 v123 aThe Wadsworth electroencephalogram (EEG)-based brain-computer interface (BCI) uses amplitude in mu or beta frequency bands over sensorimotor cortex to control cursor movement. Trained users can move the cursor in one or two dimensions. The primary goal of this research is to provide a new communication and control option for people with severe motor disabilities. Currently, cursor movements in each dimension are determined 10 times/s by an empirically derived linear function of one or two EEG features (i.e., spectral bands from different electrode locations). This study used offline analysis of data collected during system operation to explore methods for improving the accuracy of cursor movement. The data were gathered while users selected among three possible targets by controlling vertical [i.e., one-dimensional (1-D)] cursor movement. The three methods analyzed differ in the dimensionality of the cursor movement [1-D versus two-dimensional (2-D)] and in the type of the underlying function (linear versus nonlinear). We addressed two questions: Which method is best for classification (i.e., to determine from the EEG which target the user wants to hit)? How does the number of EEG features affect the performance of each method? All methods reached their optimal performance with 10-20 features. In offline simulation, the 2-D linear method and the 1-D nonlinear method improved performance significantly over the 1-D linear method. The 1-D linear method did not do so. These offline results suggest that the 1-D nonlinear or the 2-D linear cursor function will improve online operation of the BCI system.10aaugmentative communication10aBrain-computer interface (BCI)10aElectroencephalography10aFeedback1 aFabiani, Georg, E.1 aMcFarland, Dennis, J.1 aWolpaw, Jonathan1 aPfurtscheller, Gert  uhttp://www.ncbi.nlm.nih.gov/pubmed/15473195