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Spatio-temporal EEG Data Classification in the NeuCube 3D SNN Environment: Methodology and Examples

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Part of the Lecture Notes in Computer Science book series (LNTCS,volume 8228)

Abstract

A vast amount of complex spatio-temporal brain data, such as EEG-, have been accumulated. Technological advances in many disciplines rely on the proper analysis, understanding and utilisation of these data. In order to address this great challenge, the paper utilizes the recently introduced by one of the authors 3D spiking neural network environment called NeuCube for spatio-temporal EEG data classification. A methodology is proposed and illustrated on two small-scale examples: classifying EEG data for music- versus noise perception, and person identification based on music perception. Future development and usage of the NeuCube environment can be expected to significantly further the creation of novel brain-computer interfaces, cognitive robotics and medical engineering devices.

Keywords

  • EEG
  • spatio-temporal data
  • spiking neural networks
  • music perception
  • NeuCube

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Kasabov, N., Hu, J., Chen, Y., Scott, N., Turkova, Y. (2013). Spatio-temporal EEG Data Classification in the NeuCube 3D SNN Environment: Methodology and Examples. 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_9

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  • DOI: https://doi.org/10.1007/978-3-642-42051-1_9

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-42050-4

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