%0 Journal Article %J Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology %D 2011 %T The P300-based brain-computer interface (BCI): effects of stimulus rate. %A Dennis J. McFarland %A Sarnacki, William A. %A Townsend, George %A Theresa M Vaughan %A Jonathan Wolpaw %K brain-computer interface %K neuroprosthesis %K P300 %X OBJECTIVE: Brain-computer interface technology can restore communication and control to people who are severely paralyzed. We have developed a non-invasive BCI based on the P300 event-related potential that uses an 8×9 matrix of 72 items that flash in groups of 6. Stimulus presentation rate (i.e., flash rate) is one of several parameters that could affect the speed and accuracy of performance. We studied performance (i.e., accuracy and characters/min) on copy spelling as a function of flash rate. METHODS: In the first study of six BCI users, stimulus-on and stimulus-off times were equal and flash rate was 4, 8, 16, or 32 Hz. In the second study of five BCI users, flash rate was varied by changing either the stimulus-on or stimulus-off time. RESULTS: For all users, lower flash rates gave higher accuracy. The flash rate that gave the highest characters/min varied across users, ranging from 8 to 32 Hz. However, variations in stimulus-on and stimulus-off times did not themselves significantly affect accuracy. Providing feedback did not affect results in either study suggesting that offline analyses should readily generalize to online performance. However there do appear to be session-specific effects that can influence the generalizability of classifier results. CONCLUSIONS: The results show that stimulus presentation (i.e., flash) rate affects the accuracy and speed of P300 BCI performance. SIGNIFICANCE: These results extend the range over which slower flash rates increase the amplitude of the P300. Considering also presentation time, the optimal rate differs among users, and thus should be set empirically for each user. Optimal flash rate might also vary with other parameters such as the number of items in the matrix. %B Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology %V 122 %P 731–737 %8 04/2011 %G eng %U http://www.ncbi.nlm.nih.gov/pubmed/21067970 %R 10.1016/j.clinph.2010.10.029 %0 Journal Article %J Journal of motor behavior %D 2010 %T Brain-computer interface research comes of age: traditional assumptions meet emerging realities. %A Jonathan Wolpaw %K brain-computer interface %K brain-machine interface %K EEG %K human %K neuroprosthesis %X Brain-computer interfaces (BCIs) could provide important new communication and control options for people with severe motor disabilities. Most BCI research to date has been based on 4 assumptions that: (a) intended actions are fully represented in the cerebral cortex; (b) neuronal action potentials can provide the best picture of an intended action; (c) the best BCI is one that records action potentials and decodes them; and (d) ongoing mutual adaptation by the BCI user and the BCI system is not very important. In reality, none of these assumptions is presently defensible. Intended actions are the products of many areas, from the cortex to the spinal cord, and the contributions of each area change continually as the CNS adapts to optimize performance. BCIs must track and guide these adaptations if they are to achieve and maintain good performance. Furthermore, it is not yet clear which category of brain signals will prove most effective for BCI applications. In human studies to date, low-resolution electroencephalography-based BCIs perform as well as high-resolution cortical neuron-based BCIs. In sum, BCIs allow their users to develop new skills in which the users control brain signals rather than muscles. Thus, the central task of BCI research is to determine which brain signals users can best control, to maximize that control, and to translate it accurately and reliably into actions that accomplish the users' intentions. %B Journal of motor behavior %V 42 %P 351–353 %8 11/2010 %G eng %U http://www.ncbi.nlm.nih.gov/pubmed/21184352 %R 10.1080/00222895.2010.526471 %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