• 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