02447nas a2200229   4500008004100000022001400041245007700055210006700132260001200199300001400211490000800225520176000233653002901993653002002022653000902042100002602051700002602077700002102103700002402124700002102148856004802169       2011                        eng d  a1872-895200aThe P300-based brain-computer interface (BCI): effects of stimulus rate.0 aP300based braincomputer interface BCI effects of stimulus rate  c04/2011  a731–7370 v1223 aOBJECTIVE:
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.10abrain-computer interface10aneuroprosthesis10aP3001 aMcFarland, Dennis, J.1 aSarnacki, William, A.1 aTownsend, George1 aVaughan, Theresa, M1 aWolpaw, Jonathan  uhttp://www.ncbi.nlm.nih.gov/pubmed/2106797002079nas a2200205   4500008004100000022001400041245010100055210006900156260001200225300001400237490000700251520145100258653002901709653002801738653000801766653001001774653002001784100002101804856004801825       2010                        eng d  a1940-102700aBrain-computer interface research comes of age: traditional assumptions meet emerging realities.0 aBraincomputer interface research comes of age traditional assump  c11/2010  a351–3530 v423 aBrain-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.10abrain-computer interface10abrain-machine interface10aEEG10ahuman10aneuroprosthesis1 aWolpaw, Jonathan  uhttp://www.ncbi.nlm.nih.gov/pubmed/2118435201629nas a2200265   4500008004100000022001400041245010300055210006900158260001200227300001200239490000800251520078600259653003101045653002901076653002801105653002701133653002401160653002001184653001901204100001901223700002601242700002601268700002101294856004801315       2003                        eng d  a0304-394000aElectroencephalographic(EEG)-based communication: EEG control versus system performance in humans.0 aElectroencephalographicEEGbased communication EEG control versus  c07/2002  a89–920 v3453 aPeople 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.10aaugmentative communication10abrain-computer interface10abrain-machine interface10aElectroencephalography10amu and beta rhythms10aneuroprosthesis10aRehabilitation1 aSheikh, Hesham1 aMcFarland, Dennis, J.1 aSarnacki, William, A.1 aWolpaw, Jonathan  uhttp://www.ncbi.nlm.nih.gov/pubmed/12821178