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Brain Disease Diagnosis and Prognosis Based on EEG Data

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Time-Space, Spiking Neural Networks and Brain-Inspired Artificial Intelligence

Part of the book series: Springer Series on Bio- and Neurosystems ((SSBN,volume 7))

Abstract

This chapter applies the methodology for learning and pattern recognition with BI-SNN, introduced in Chap. 8 on EEG data measuring changes in brain states due to a brain disease or treatment.

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Acknowledgements

Some of the material in this chapter has been first published in journal and conference publications as referenced and cited in corresponding sections and sub-sections and also in book volumes [2, 16, 23, 24]. I acknowledge the great contribution of my co-authors of these publications Maryam Doborjeh, Elisa Capecci, Zohreh Doborjeh, Nathan Scott, Carlo Morabito, Nadia Mammone, F. La Foresta, Grace Wang.

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Authors and Affiliations

  1. Knowledge Engineering and Discovery Research Institute (KEDRI), Auckland University of Technology, Auckland, New Zealand

    Nikola K. Kasabov

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  1. Nikola K. Kasabov
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Correspondence to Nikola K. Kasabov .

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Kasabov, N.K. (2019). Brain Disease Diagnosis and Prognosis Based on EEG Data. In: Time-Space, Spiking Neural Networks and Brain-Inspired Artificial Intelligence . Springer Series on Bio- and Neurosystems, vol 7. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-57715-8_9

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