Abstract
The paper presents a method for the classification of EEG data recorded in two cognitive scenarios, a relaxing and memory task. The method uses a reservoir of spiking neurons that are activated by the spatio-temporal EEG data. The states of the reservoir are periodically read out and classified producing in a continuous classification result over time. After suitable optimization of the model parameters, we achieve a test accuracy of 82% on a small data set. Future applications of the proposed model are discussed including its use for an early detection of a cognitive impairment such as in Alzheimers disease.
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References
Buteneers, P., Verstraeten, D., Nieuwenhuyse, B.V., Stroobandt, D., Raedt, R., Vonck, K., Boon, P., Schrauwen, B.: Real-time detection of epileptic seizures in animal models using reservoir computing. Epilepsy Research 103(2-3), 124–134 (2013)
von der Elst, W., van Boxtel, M.J., van Breukelen, G.P., Jolles, J.: Assessment of information processing in working memory in applied settings: the paper and pencil memory scanning test. Psychological Medicine 37, 1335–1344 (2007)
Gerstner, W., Kistler, W.M.: Spiking Neuron Models: Single Neurons, Populations, Plasticity. Cambridge University Press, Cambridge (2002)
Jaeger, H.: The “echo state” approach to analysing and training recurrent neural networks. Tech. rep., Fraunhofer Institute for Autonomous Intelligent Syst. (2001)
Kasabov, N.: Neucube evospike architecture for spatio-temporal modelling and pattern recognition of brain signals. In: Mana, N., Schwenker, F., Trentin, E. (eds.) ANNPR 2012. LNCS (LNAI), vol. 7477, pp. 225–243. Springer, Heidelberg (2012)
Kindermans, P.J., Buteneers, P., Verstraeten, D., Schrauwen, B.: An uncued brain-computer interface using reservoir computing. In: Workshop: Machine Learning for Assistive Technologies, Proceedings, p. 8. Ghent University, Department of Electronics and information systems (2010)
Maass, W., Natschläger, T., Markram, H.: Real-time computing without stable states: A new framework for neural computation based on perturbations. Neural Computation 14(11), 2531–2560 (2002)
Markram, H., Wang, Y., Tsodyks, M.: Differential signaling via the same axon of neocortical pyramidal neurons. Proceedings of the National Academy of Sciences 95(9), 5323–5328 (1998)
Schliebs, S., Defoin-Platel, M., Kasabov, N.: Integrated feature and parameter optimization for an evolving spiking neural network. In: Köppen, M., Kasabov, N., Coghill, G. (eds.) ICONIP 2008, Part I. LNCS, vol. 5506, pp. 1229–1236. Springer, Heidelberg (2009)
Schliebs, S., Fiasché, M., Kasabov, N.: Constructing robust liquid state machines to process highly variable data streams. In: Villa, A.E.P., Duch, W., Érdi, P., Masulli, F., Palm, G. (eds.) ICANN 2012, Part I. LNCS, vol. 7552, pp. 604–611. Springer, Heidelberg (2012)
Schliebs, S., Hunt, D.: Continuous classification of spatio-temporal data streams using liquid state machines. In: Huang, T., Zeng, Z., Li, C., Leung, C.S. (eds.) ICONIP 2012, Part IV. LNCS, vol. 7666, pp. 626–633. Springer, Heidelberg (2012)
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Schliebs, S., Capecci, E., Kasabov, N. (2013). Spiking Neural Network for On-line Cognitive Activity Classification Based on EEG Data. In: Lee, M., Hirose, A., Hou, ZG., Kil, R.M. (eds) Neural Information Processing. ICONIP 2013. Lecture Notes in Computer Science, vol 8228. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-42051-1_8
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DOI: https://doi.org/10.1007/978-3-642-42051-1_8
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-42050-4
Online ISBN: 978-3-642-42051-1
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