Exploring optimal codes and ensemble classifiers in a c-VEP BCI

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Brain-computer interfaces (BCIs) are used to interact with a computer using brain activity. BCIs based on code-modulated visual evoked potentials (c-VEPs), whereby the background of targets flickers with different codes, have recently shown the ability to outperform other methods in terms of accuracy and speed. In a speller application, electroencephalogram (EEG) data is recorded from the user while they focus on a target. Though c-VEP-based BCIs outperform other methods, the accuracy and speed could still be improved. The present research aims is to improve the accuracy and speed of classification in an offline analysis of two datasets. First, to explore improving the accuracy, combining multiple canonical correlation analysis (CCA, a feature extractor) components in an ensemble classifier is shown to not significantly improve accuracy. Secondly, to improve speed, it is found that there is no significant difference in the information transfer rate (ITR) of Gold codes within a set. With a similar goal, amount of neighbours is found to not have a significant effect on the accuracy of Gold codes. The combination of finding that there is no significant difference in ITR of Gold codes within a set, and their low cross-correlation properties shows there is no bias in either. Furthermore, it is found that the amount of neighbours does not significantly affect Gold codes. Further research could focus on testing these findings on other popular code families.
Faculteit der Sociale Wetenschappen