Brain-Computer Interfacing to Heuristic Search: First Results

This data was imported from Scopus:

Authors: Cavazza, M., Aranyi, G. and Charles, F.

Journal: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volume: 9107

Pages: 312-321

eISSN: 1611-3349

ISBN: 9783319189130

ISSN: 0302-9743

DOI: 10.1007/978-3-319-18914-7_33

© Springer International Publishing Switzerland 2015. 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.

This data was imported from Web of Science (Lite):

Authors: Cavazza, M., Aranyi, G. and Charles, F.

Journal: ARTIFICIAL COMPUTATION IN BIOLOGY AND MEDICINE, PT I (IWINAC 2015)

Volume: 9107

Pages: 312-321

ISBN: 978-3-319-18913-0

ISSN: 0302-9743

DOI: 10.1007/978-3-319-18914-7_33

The data on this page was last updated at 04:55 on March 18, 2019.