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Evolving Probabilistic Spiking Neural Networks: Modelling and Pattern Recognition of Spatio-temporal Brain Data (EEG) - Couverture souple
Synopsis
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.
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Présentation de l'éditeur
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.
Biographie de l'auteur
Nuttapod graduated a M.Sc. in Computer Science from King Mongkut’s Institute of Technology,Thailand with outstanding thesis award. He completed a PhD in Computer and Information Science from Auckland University of Technology, New Zealand under the supervision of Prof. Nikola Kasabov and Assoc. Prof. Petia Georgieva.
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Evolving Probabilistic Spiking Neural Networks
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LAP LAMBERT Academic Publishing, 2013
ISBN 10 : 3659430803
ISBN 13 : 9783659430800
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Evolving Probabilistic Spiking Neural Networks
Edité par
LAP LAMBERT Academic Publishing, 2013
ISBN 10 : 3659430803
ISBN 13 : 9783659430800
Neuf
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Evolving Probabilistic Spiking Neural Networks: Modelling and Pattern Recognition of Spatio-temporal Brain Data (EEG)
Edité par
LAP LAMBERT Academic Publishing, 2013
ISBN 10 : 3659430803
ISBN 13 : 9783659430800
Neuf
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Evolving Probabilistic Spiking Neural Networks Modelling and Pattern Recognition of Spatiotemporal Brain Data EEG
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LAP LAMBERT Academic Publishing, 2013
ISBN 10 : 3659430803
ISBN 13 : 9783659430800
Neuf
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Evolving Probabilistic Spiking Neural Networks: Modelling and Pattern Recognition of Spatio-temporal Brain Data (EEG)
Edité par
LAP LAMBERT Academic Publishing, 2013
ISBN 10 : 3659430803
ISBN 13 : 9783659430800
Neuf
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Evolving Probabilistic Spiking Neural Networks : Modelling and Pattern Recognition of Spatio-temporal Brain Data (EEG)
Edité par
LAP LAMBERT Academic Publishing, 2013
ISBN 10 : 3659430803
ISBN 13 : 9783659430800
Neuf
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Taschenbuch. Etat : Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 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. N° de réf. du vendeur 9783659430800
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Evolving Probabilistic Spiking Neural Networks
Edité par
LAP LAMBERT Academic Publishing Jul 2013, 2013
ISBN 10 : 3659430803
ISBN 13 : 9783659430800
Neuf
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Vendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Allemagne
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Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - 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. 256 pp. Englisch. N° de réf. du vendeur 9783659430800
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Evolving Probabilistic Spiking Neural Networks
Edité par
LAP LAMBERT Academic Publishing Jul 2013, 2013
ISBN 10 : 3659430803
ISBN 13 : 9783659430800
Neuf
Taschenbuch
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Taschenbuch. Etat : Neu. 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. N° de réf. du vendeur 9783659430800
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Evolving Probabilistic Spiking Neural Networks: Modelling and Pattern Recognition of Spatio-temporal Brain Data (EEG)
Edité par
LAP LAMBERT Academic Publishing, 2013
ISBN 10 : 3659430803
ISBN 13 : 9783659430800
Neuf
Couverture souple
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