Abstract:
Brain-Computer Interfaces (BCI) are a means to communicate to a digital device without the
use of the peripheral nervous system. Noise tagging is a promising and relatively new
technique that could provide high speed communication, but so far it has mainly been tested
using common static squares. In this paper, I have explored the effects of stimulus shape and
movement on the performance of a BCI. Animated figures could be used as targets in BCI
controlled gaming, and be used to integrate BCI into the environment in an intuitive way.
There was no significant effect found of either shape or movement on the performance, and
the success of cross-condition classification opens up the possibility of using targets of
different shapes with different animations at the same time without having to train different
classifiers for each target type.
