Brain-computer interfacing to heuristic search: First results

Abstract

We explore a novel approach in which BCI input is used to influence the behaviour of search algorithms which are at the heart of many Intelligent Systems. We describe how users can influence the behaviour of heuristic search algorithms using Neurofeedback (NF), establishing a connection between their mental disposition and the performance of the search process. More specifically, we used functional near-infrared spectroscopy (fNIRS) to measure frontal asymmetry as a marker of approach and risk acceptance under a NF paradigm, in which users increased their left asymmetry. Their input was mapped onto a dynamic weighting implementation of A* (termed WA*), modifying the behaviour of the algorithm during the resolution of an 8-puzzle problem by adjusting the performance-optimality tradeoff. We tested this approach with a proofof- concept experiment involving 11 subjects who had been previously trained in NF. Subjects were able to positively influence the behaviour of the search process in over 58% of the NF epochs, resulting in faster solutions.