Evolving Probabilistic Spiking Neural Networks

Nuttapod Nuntalid

ISBN 10: 3659430803 / ISBN 13: 9783659430800

Verlag: LAP LAMBERT Academic Publishing Jul 2013, 2013

Beschreibung

This item is printed on demand - Print on Demand Titel. Neuware -The use of Electroencephalography (EEG) in Brain Computer Interface (BCI) domain presents a challenging problem due to presence of spatial and temporal aspects inherent in the EEG data. Many studies either transform the data into a temporal or spatial problem for analysis. This approach results in loss of significant information since these methods fail to consider the correlation present within the spatial and temporal aspect of the EEG data. However, Spiking Neural Network (SNN) naturally takes into consideration the correlation present within the spatio-temporal data. Hence by applying the proposed SNN based novel methods on EEG, the thesis provide improved analytic on EEG data. This book introduces novel methods and architectures for spatio-temporal data modelling and classification using SNN. More specifically, SNN is used for analysis and classification of spatiotemporal EEG data.Books on Demand GmbH, Überseering 33, 22297 Hamburg 256 pp. Englisch.

Bestandsnummer des Verkäufers 9783659430800

Bibliografische Details

• Titel: Evolving Probabilistic Spiking Neural Networks
• Verlag: LAP LAMBERT Academic Publishing Jul 2013
• Erscheinungsjahr: 2013
• Sprache: Englisch
• ISBN-10: 3659430803
• ISBN-13: 9783659430800
• Einband: Taschenbuch
• Zustand: Neu

Über diesen Titel

Inhaltsangabe

The use of Electroencephalography (EEG) in Brain Computer Interface (BCI) domain presents a challenging problem due to presence of spatial and temporal aspects inherent in the EEG data. Many studies either transform the data into a temporal or spatial problem for analysis. This approach results in loss of significant information since these methods fail to consider the correlation present within the spatial and temporal aspect of the EEG data. However, Spiking Neural Network (SNN) naturally takes into consideration the correlation present within the spatio-temporal data. Hence by applying the proposed SNN based novel methods on EEG, the thesis provide improved analytic on EEG data. This book introduces novel methods and architectures for spatio-temporal data modelling and classification using SNN. More specifically, SNN is used for analysis and classification of spatiotemporal EEG data.