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Evolving Processes in Time-Space. Deep Learning and Deep Knowledge Representation in Time-Space. Brain-Inspired AI Read chapter Read first chapter

2019 | OriginalPaper | Chapter

8.  Deep Learning and Deep Knowledge Representation of EEG Data

Author : Nikola K. Kasabov

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

Publisher: Springer Berlin Heidelberg

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Abstract

This chapter presents general methods for deep learning and deep knowledge representation of EEG data in brain-inspired SNN (BI-SNN). These methods are applied to develop specific methods for EEG data analysis and for modelling brain cognitive functions, such as: performing cognitive tasks; emotion recognition from face expression; sub-conscious processing of stimuli; modelling attentional bias.

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previous chapter Evolutionary- and Quantum-Inspired Computation. Applications for SNN Optimisation
next chapter Brain Disease Diagnosis and Prognosis Based on EEG Data
Appendix
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Metadata
Title
Deep Learning and Deep Knowledge Representation of EEG Data
Author
Nikola K. Kasabov
Copyright Year
2019
Publisher
Springer Berlin Heidelberg
Book
Time-Space, Spiking Neural Networks and Brain-Inspired Artificial Intelligence

Print ISBN: 978-3-662-57713-4

Electronic ISBN: 978-3-662-57715-8

Copyright Year: 2019

DOI
https://doi.org/10.1007/978-3-662-57715-8_8

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