A Comparative Analysis of Correlation and Correntropy in Graph-Based Brain Computer Interfaces

This work presents a comparative analysis of correlation and correntropy in the context of graph-based braincomputer interfaces using motor imagery. These two statistical entities are used in the construction of the graphs, from which features are extracted. The results indicate that correntropy has a more consistent performance over the different graph measures, hence deserving to be considered as a relevant option by researchers of the field.