02490nas a2200265   4500008004100000022001400041245004000055210003800095260001200133300001400145490000700159520177200166653002801938653002701966653001401993653002502007653001502032653001902047653002402066100002102090700001902111700002102130700002602151856004702177       1997                        eng d  a0736-025800aTiming of EEG-based cursor control.0 aTiming of EEGbased cursor control  c11/1997  a529–5380 v143 aRecent studies show that humans can learn to control the amplitude of electroencephalography (EEG) activity in specific frequency bands over sensorimotor cortex and use it to move a cursor to a target on a computer screen. EEG-based communication could be a valuable new communication and control option for those with severe motor disabilities. Realization of this potential requires detailed knowledge of the characteristic features of EEG control. This study examined the course of EEG control after presentation of a target. At the beginning of each trial, a target appeared at the top or bottom edge of the subject's video screen and 1 sec later a cursor began to move vertically as a function of EEG amplitude in a specific frequency band. In well-trained subjects, this amplitude was high at the time the target appeared and then either remained high (i.e., for a top target) or fell rapidly (i.e., for a bottom target). Target-specific EEG amplitude control began 0.5 sec after the target appeared and appeared to wax and wane with a period of approximately 1 sec until the cursor reached the target (i.e., a hit) or the opposite edge of the screen (i.e., a miss). Accuracy was 90% or greater for each subject. Top-target errors usually occurred later in the trial because of failure to reach and/or maintain sufficiently high amplitude, whereas bottom-target errors usually occurred immediately because of failure to reduce an initially high amplitude quickly enough. The results suggest modifications that could improve performance. These include lengthening the intertrial period, shortening the delay between target appearance and cursor movement, and including time within the trial as a variable in the equation that translates EEG into cursor movement.10aassistive communication10aElectroencephalography10amu rhythm10aoperant conditioning10aprosthesis10aRehabilitation10asensorimotor cortex1 aWolpaw, Jonathan1 aFlotzinger, D.1 aPfurtscheller, G1 aMcFarland, Dennis, J.  uhttp://www.ncbi.nlm.nih.gov/pubmed/945806001530nas a2200205   4500008004100000022001400041245011100055210006900166260000900235300001400244490000700258520087900265653002801144100002101172700001901193700001801212700002101230700002601251856004701277       1995                        eng d  a0047-655200aEEG-based brain computer interface (BCI). Search for optimal electrode positions and frequency components.0 aEEGbased brain computer interface BCI Search for optimal electro  c1996  a111–1210 v213 aSeveral laboratories around the world have recently started to investigate EEG-based brain computer interface (BCI) systems in order to create a new communication channel for subjects with severe motor impairments. The present paper describes an initial evaluation of 64-channel EEG data recorded while subjects used one EEG channel over the left sensorimotor area to control on-line vertical cursor movement. Targets were given at the top or bottom of a computer screen. Data from 3 subjects in the early stages of training were analyzed by calculating band power time courses and maps for top and bottom targets separately. In addition, the Distinction Sensitive Learning Vector Quantizer (DSLVQ) was applied to single-trial EEG data. It was found that for each subject there exist optimal electrode positions and frequency components for on-line EEG-based cursor control.10aUser-Computer Interface1 aPfurtscheller, G1 aFlotzinger, D.1 aPregenzer, M.1 aWolpaw, Jonathan1 aMcFarland, Dennis, J.  uhttp://www.ncbi.nlm.nih.gov/pubmed/8776708