DocumentCode :
3684422
Title :
Effects of feedback latency on P300-based brain-computer interface
Author :
Mahnaz Arvaneh;Tomas E. Ward;Ian H. Robertson
Author_Institution :
Trinity College Institute of Neuroscience, and Insight Centre for Data Analytics, Dublin, Ireland
fYear :
2015
Firstpage :
2315
Lastpage :
2318
Abstract :
Feedback has been shown to affect performance when using a Brain-Computer Interface (BCI) based on sensorimotor rhythms. In contrast, little is known about the influence of feedback on P300-based BCIs. There is still an open question whether feedback affects the regulation of P300 and consequently the operation of P300-based BCIs. In this paper, for the first time, the influence of feedback on the P300-based BCI speller task is systematically assessed. For this purpose, 24 healthy participants performed the classic P300-based BCI speller task, while only half of them received feedback. Importantly, the number of flashes per letter was reduced on a regular basis in order to increase the frequency of providing feedback. Experimental results showed that feedback could significantly improve the P300-based BCI speller performance, if it was provided in short time intervals (e.g. in sequences as short as 4 to 6 flashes per row/column). Moreover, our offline analysis showed that providing feedback remarkably enhanced the relevant ERP patterns and attenuated the irrelevant ERP patterns, such that the discrimination between target and non-target EEG trials increased.
Keywords :
"Ash","Electroencephalography","Training","Signal to noise ratio","Brain-computer interfaces","Accuracy","Calibration"
Publisher :
ieee
Conference_Titel :
Engineering in Medicine and Biology Society (EMBC), 2015 37th Annual International Conference of the IEEE
ISSN :
1094-687X
Electronic_ISBN :
1558-4615
Type :
conf
DOI :
10.1109/EMBC.2015.7318856
Filename :
7318856
Link To Document :
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