Exploring code families and event-types for cVEP BCIs

Abstract

A brain-computer interface (BCI) is a system that makes it possible for a user to interact with a computer without muscular control. Code-modulated visual evoked potential (cVEP) BCI can be used to create visual speller systems. To make a step towards practical, real-world cVEP BCI by improving performance and reducing calibration time, this thesis compares three methods of decomposing codes, called event-types, when using the reconvolution method by Thielen et al. (2015) [12] to generate templates. This is done on two di erent data sets, one using random stimulation codes and one using modulated Gold codes. It is found that event-types that take into account the nonlinearity of the visual system perform better in terms of classi cation accuracy, auto explained variance and cross explained variance than event-types that assume linearity. It is concluded that using the edge responses of a ash as an event-type is the most practical approach, as it models only two events and therefore requires less training data than event-types with more events. The contrast event-type is also more versatile, as it does not require events in the validation set to have appeared in the training set.