A collaborative Brain-Computer Interface to improve human performance in a visual search task

In this paper we use a collaborative brain-computer interface to integrate the decision confidence of multiple non-communicating observers as a mechanism to improve group decisions. In recent research we tested this idea with the decisions associated with a simple visual matching task and found that a collaborative BCI can outperform group decisions made by a majority vote. Here we extend these initial findings in two ways. Firstly, we look at a more traditional (and more difficult) visual search task involving deciding whether a red vertical bar is present in a random set of 40 red and green, horizontal and vertical bars shown for a very short time. Secondly, to extract features from the neural signals we use spatial CSP filters instead of the spatio-temporal PCA we used in previous research, resulting in a significant reduction in the number of features and free parameters used in the system. Results obtained with 10 participants indicate that for almost all group sizes our new CSP-based collaborative BCI yields group decisions that are statistically significantly better than both traditional (majority-based) group decisions and group decisions made by a PCA-based collaborative BCI.