@article {3096,
	title = {The P300-based brain-computer interface (BCI): effects of stimulus rate.},
	journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology},
	volume = {122},
	year = {2011},
	month = {04/2011},
	pages = {731{\textendash}737},
	abstract = {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{\texttimes}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.},
	keywords = {brain-computer interface, neuroprosthesis, P300},
	issn = {1872-8952},
	doi = {10.1016/j.clinph.2010.10.029},
	url = {http://www.ncbi.nlm.nih.gov/pubmed/21067970},
	author = {Dennis J. McFarland and Sarnacki, William A. and Townsend, George and Theresa M Vaughan and Jonathan Wolpaw}
}
@article {3145,
	title = {Electroencephalographic(EEG)-based communication: EEG control versus system performance in humans.},
	journal = {Neuroscience letters},
	volume = {345},
	year = {2003},
	month = {07/2002},
	pages = {89{\textendash}92},
	abstract = {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.},
	keywords = {augmentative communication, brain-computer interface, brain-machine interface, Electroencephalography, mu and beta rhythms, neuroprosthesis, Rehabilitation},
	issn = {0304-3940},
	doi = {10.1016/S0304-3940(03)00470-1},
	url = {http://www.ncbi.nlm.nih.gov/pubmed/12821178},
	author = {Sheikh, Hesham and Dennis J. McFarland and Sarnacki, William A. and Jonathan Wolpaw}
}
