%0 Journal Article %J Proceedings of the National Academy of Sciences of the United States of America %D 2004 %T Control of a two-dimensional movement signal by a noninvasive brain-computer interface in humans. %A Jonathan Wolpaw %A Dennis J. McFarland %K brain-machine interface %K Electroencephalography %X Brain-computer interfaces (BCIs) can provide communication and control to people who are totally paralyzed. BCIs can use noninvasive or invasive methods for recording the brain signals that convey the user's commands. Whereas noninvasive BCIs are already in use for simple applications, it has been widely assumed that only invasive BCIs, which use electrodes implanted in the brain, can provide multidimensional movement control of a robotic arm or a neuroprosthesis. We now show that a noninvasive BCI that uses scalp-recorded electroencephalographic activity and an adaptive algorithm can provide humans, including people with spinal cord injuries, with multidimensional point-to-point movement control that falls within the range of that reported with invasive methods in monkeys. In movement time, precision, and accuracy, the results are comparable to those with invasive BCIs. The adaptive algorithm used in this noninvasive BCI identifies and focuses on the electroencephalographic features that the person is best able to control and encourages further improvement in that control. The results suggest that people with severe motor disabilities could use brain signals to operate a robotic arm or a neuroprosthesis without needing to have electrodes implanted in their brains. %B Proceedings of the National Academy of Sciences of the United States of America %V 101 %P 17849–17854 %8 12/2004 %G eng %U http://www.ncbi.nlm.nih.gov/pubmed/15585584 %R 10.1073/pnas.0403504101 %0 Journal Article %J Neuroscience letters %D 2003 %T Electroencephalographic(EEG)-based communication: EEG control versus system performance in humans. %A Sheikh, Hesham %A Dennis J. McFarland %A Sarnacki, William A. %A Jonathan Wolpaw %K augmentative communication %K brain-computer interface %K brain-machine interface %K Electroencephalography %K mu and beta rhythms %K neuroprosthesis %K Rehabilitation %X People can learn to control electroencephalographic (EEG) sensorimotor rhythm amplitude so as to move a cursor to select among choices on a computer screen. We explored the dependence of system performance on EEG control. Users moved the cursor to reach a target at one of four possible locations. EEG control was measured as the correlation (r(2)) between rhythm amplitude and target location. Performance was measured as accuracy (% of targets hit) and as information transfer rate (bits/trial). The relationship between EEG control and accuracy can be approximated by a linear function that is constant for all users. The results facilitate offline predictions of the effects on performance of using different EEG features or combinations of features to control cursor movement. %B Neuroscience letters %V 345 %P 89–92 %8 07/2002 %G eng %U http://www.ncbi.nlm.nih.gov/pubmed/12821178 %R 10.1016/S0304-3940(03)00470-1