Skip navigation

BCI control of heuristic search algorithms

BCI control of heuristic search algorithms

Cavazza, Marc ORCID: 0000-0001-6113-9696, Aranyi, Gabor and Charles, Fred (2017) BCI control of heuristic search algorithms. Frontiers in Neuroinformatics, 11:6. pp. 1-11. ISSN 1662-5196 (Online) (doi:

PDF (Publisher's PDF - Open Access)
19811 CAVAZZA_BCI_Control_of_Heuristic_Search_Algorithms_2017.pdf - Published Version
Available under License Creative Commons Attribution.

Download (3MB) | Preview


The ability to develop Brain-Computer Interfaces (BCI) to Intelligent Systems would offer new perspectives in terms of human supervision of complex Artificial Intelligence (AI) systems, as well as supporting new types of applications. In this article, we introduce a basic mechanism for the control of heuristic search through fNIRS-based BCI. The rationale is that heuristic search is not only a basic AI mechanism but also one still at the heart of many different AI systems. We investigate how users’ mental disposition can be harnessed to influence the performance of heuristic search algorithm through a mechanism of precision-complexity exchange. From a system perspective, we use weighted variants of the A* algorithm which have an ability to provide faster, albeit suboptimal solutions. We use recent results in affective BCI to capture a BCI signal, which is indicative of a compatible mental disposition in the user. It has been established that Prefrontal Cortex (PFC) asymmetry is strongly correlated to motivational dispositions and results anticipation, such as approach or even risk-taking, and that this asymmetry is amenable to Neurofeedback (NF) control. Since PFC asymmetry is accessible through fNIRS, we designed a BCI paradigm in which users vary their PFC asymmetry through NF during heuristic search tasks, resulting in faster solutions. This is achieved through mapping the PFC asymmetry value onto the dynamic weighting parameter of the weighted A* (WA*) algorithm. We illustrate this approach through two different experiments, one based on solving 8-puzzle configurations, and the other on path planning. In both experiments, subjects were able to speed up the computation of a solution through a reduction of search space in WA*. Our results establish the ability of subjects to intervene in heuristic search progression, with effects which are commensurate to their control of PFC asymmetry: this opens the way to new mechanisms for the implementation of hybrid cognitive systems.

Item Type: Article
Additional Information: Copyright © 2017 Cavazza, Aranyi and Charles. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution and reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
Uncontrolled Keywords: user interfaces, brain-computer interfaces (BCI), neurofeedback (NF), functional near-infrared spectroscopy (fNIRS), heuristic search
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Faculty / School / Research Centre / Research Group: Faculty of Engineering & Science > School of Computing & Mathematical Sciences (CMS)
Faculty of Engineering & Science
Last Modified: 04 Mar 2022 13:07

Actions (login required)

View Item View Item


Downloads per month over past year

View more statistics