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 im- plementation 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 proof-of-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.
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References
Ayaz, H.: Functional near infrared spectroscopy based brain computer interface. PhD thesis, Drexel University (2010)
Baehr, E., Rosenfeld, J.P., Baehr, R.: Clinical use of an alpha asymmetry neurofeedback protocol in the treatment of mood disorders: Follow-up study one to five years post therapy. Journal of Neurotherapy 4(4), 11–18 (2001)
Bunce, S.C., Izzetoglu, M., Izzetoglu, K., Onaral, B., Pourrezaei, K.: Functional near-infrared spectroscopy. IEEE Engineering in Medicine and Biology Magazine 25(4), 54–62 (2006)
Cavazza, M., Charles, F., Aranyi, G., Porteous, J., Gilroy, S.W., Raz, G., Keynan, N.J., Cohen, A., Jackont, G., Jacob, Y., et al.: Towards emotional regulation through neurofeedback. In: Proceedings of the 5th Augmented Human International Conference, p. 42. ACM (2014)
Davidson, R.J., Ekman, P., Saron, C.D., Senulis, J.A., Friesen, W.V.: Approach-withdrawal and cerebral asymmetry: Emotional expression and brain physiology: I. Journal of Personality and Social Psychology 58(2), 330–341 (1990)
Doi, H., Nishitani, S., Shinohara, K.: NIRS as a tool for assaying emotional function in the prefrontal cortex. Frontiers in Human Neuroscience 7 (2013)
Ebendt, R., Drechsler, R.: Weighted A* search–unifying view and application. Artificial Intelligence 173(14), 1310–1342 (2009)
Eugster, M.J., Ruotsalo, T., Spapé, M.M., Kosunen, I., Barral, O., Ravaja, N., Jacucci, G., Kaski, S.: Predicting term-relevance from brain signals. In: Proceedings of the 37th International ACM SIGIR Conference on Research & Development in Information Retrieval, pp. 425–434. ACM (2014)
Gerson, A.D., Parra, L.C., Sajda, P.: Cortically coupled computer vision for rapid image search. IEEE Transactions on Neural Systems and Rehabilitation Engineering 14(2), 174–179 (2006)
Girouard, A., Solovey, E.T., Hirshfield, L.M., Chauncey, K., Sassaroli, A., Fantini, S., Jacob, R.J.K.: Distinguishing difficulty levels with non-invasive brain activity measurements. In: Gross, T., Gulliksen, J., Kotzé, P., Oestreicher, L., Palanque, P., Prates, R.O., Winckler, M. (eds.) INTERACT 2009. LNCS, vol. 5726, pp. 440–452. Springer, Heidelberg (2009)
Hansen, E.A., Zhou, R.: Anytime heuristic search. J. Artif. Intell. Res(JAIR) 28, 267–297 (2007)
Kapoor, A., Shenoy, P., Tan, D.: Combining brain computer interfaces with vision for object categorization. In: Conference on Computer Vision and Pattern Recognition, pp. 1–8. IEEE (2008)
Nijholt, A., Tan, D.: Brain-computer interfacing for intelligent systems. IEEE Intelligent Systems 23(3), 72–79 (2008)
Pearl, J.: Heuristics: intelligent search strategies for computer problem solving. Addison-Wesley Pub. Co., Inc., Reading (1984)
Pohl, I.: Heuristic search viewed as path finding in a graph. Artificial Intelligence 1(3), 193–204 (1970)
Reinefeld, A.: Complete solution of the eight-puzzle and the benefit of node-ordering in IDA*. In: Procs. Int. Joint Conf. on AI, Chambery, Savoie, France, pp. 248–253 (September 1993)
Ruocco, A.C., Rodrigo, A.H., Lam, J., Di Domenico, S.I., Graves, B., Ayaz, H.: A problem-solving task specialized for functional neuroimaging: validation of the Scarborough adaptation of the Tower of London (S-TOL) using near-infrared spectroscopy. Frontiers in Human Neuroscience 8 (2014)
Sakatani, K., Takemoto, N., Tsujii, T., Yanagisawa, K., Tsunashima, H.: NIRS-based neurofeedback learning systems for controlling activity of the prefrontal cortex. In: Oxygen Transport to Tissue XXXV, pp. 449–454. Springer (2013)
Solovey, E., Schermerhorn, P., Scheutz, M., Sassaroli, A., Fantini, S., Jacob, R.: Brainput: enhancing interactive systems with streaming fNIRS brain input. In: SIGCHI Conference on Human Factors in Computing Systems, pp. 2193–2202. ACM (2012)
Solovey, E.T., Girouard, A., Chauncey, K., Hirshfield, L.M., Sassaroli, A., Zheng, F., Fantini, S., Jacob, R.J.: Using fNIRS brain sensing in realistic HCI settings: experiments and guidelines. In: Proceedings of the 22nd Annual ACM Symposium on User Interface Software and Technology, pp. 157–166. ACM (2009)
Studer, B., Pedroni, A., Rieskamp, J.: Predicting risk-taking behavior from prefrontal resting-state activity and personality. PLoS One 8(10), e76861 (2013)
Sutton, S.K., Davidson, R.J.: Prefrontal brain asymmetry: A biological substrate of the behavioral approach and inhibition systems. Psychological Science 8(3), 204–210 (1997)
Zotev, V., Krueger, F., Phillips, R., Alvarez, R.P., Simmons, W.K., Bellgowan, P., Drevets, W.C., Bodurka, J.: Self-regulation of amygdala activation using real-time fMRI neurofeedback. PloS one 6(9), e24522 (2011)
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Cavazza, M., Aranyi, G., Charles, F. (2015). Brain-Computer Interfacing to Heuristic Search: First Results. In: Ferrández Vicente, J., Álvarez-Sánchez, J., de la Paz López, F., Toledo-Moreo, F., Adeli, H. (eds) Artificial Computation in Biology and Medicine. IWINAC 2015. Lecture Notes in Computer Science(), vol 9107. Springer, Cham. https://doi.org/10.1007/978-3-319-18914-7_33
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DOI: https://doi.org/10.1007/978-3-319-18914-7_33
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