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A Modal Approach to the Space-Time Dynamics of Cognitive Biomarkers [electronic resource] / by Tristan D. Griffith, James E. Hubbard Jr., Mark J. Balas.

By: Griffith, Tristan D [author.].
Contributor(s): Hubbard Jr., James E [author.] | Balas, Mark J [author.] | SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: Synthesis Lectures on Biomedical Engineering: Publisher: Cham : Springer International Publishing : Imprint: Springer, 2023Edition: 1st ed. 2023.Description: XIII, 132 p. 40 illus., 31 illus. in color. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783031235290.Subject(s): Biomedical engineering | Computational neuroscience | Biochemical markers | Cognitive neuroscience | Neural networks (Computer science)  | Biomedical Engineering and Bioengineering | Computational Neuroscience | Biomarkers | Cognitive Neuroscience | Mathematical Models of Cognitive Processes and Neural NetworksAdditional physical formats: Printed edition:: No title; Printed edition:: No title; Printed edition:: No titleDDC classification: 610.28 Online resources: Click here to access online
Contents:
1. Introduction -- 2. A Dynamic Systems View of Brain Waves -- 3. System Identification of Brain Wave Modes Using EEG -- 4. Modal Analysis of Brain Wave Dynamics -- 5. Adaptive Unknown Input Estimators -- 6. Reconstructing the Brain Wave Unknown Input -- 7. Conclusions and Future Work.
In: Springer Nature eBookSummary: This book develops and details a rigorous, canonical modeling approach for analyzing spatio-temporal brain wave dynamics. The nonlinear, nonstationary behavior of brain wave measures and general uncertainty associated with the brain makes it difficult to apply modern system identification techniques to such systems. While there is a substantial amount of literature on the use of stationary analyses for brain waves, relatively less work has considered real-time estimation and imaging of brain waves from noninvasive measurements. This book addresses the issue of modeling and imaging brain waves and biomarkers generally, treating the nonlinear and nonstationary dynamics in near real-time. Using a modal state-space formulation leads to intuitive, physically significant models which are used for analysis and diagnosis. A Modal Approach to the Space-Time Dynamics of Cognitive Biomarkers provides a much-needed reference for practicing researchers in biomarker modeling leveraging the lens of engineering dynamics. Bridges the gap between neuroscience and engineering tools; Reveals space-time dynamics of brain waves via modal analysis and imaging; Addresses nonlinear and stochastic brain wave dynamics.
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1. Introduction -- 2. A Dynamic Systems View of Brain Waves -- 3. System Identification of Brain Wave Modes Using EEG -- 4. Modal Analysis of Brain Wave Dynamics -- 5. Adaptive Unknown Input Estimators -- 6. Reconstructing the Brain Wave Unknown Input -- 7. Conclusions and Future Work.

This book develops and details a rigorous, canonical modeling approach for analyzing spatio-temporal brain wave dynamics. The nonlinear, nonstationary behavior of brain wave measures and general uncertainty associated with the brain makes it difficult to apply modern system identification techniques to such systems. While there is a substantial amount of literature on the use of stationary analyses for brain waves, relatively less work has considered real-time estimation and imaging of brain waves from noninvasive measurements. This book addresses the issue of modeling and imaging brain waves and biomarkers generally, treating the nonlinear and nonstationary dynamics in near real-time. Using a modal state-space formulation leads to intuitive, physically significant models which are used for analysis and diagnosis. A Modal Approach to the Space-Time Dynamics of Cognitive Biomarkers provides a much-needed reference for practicing researchers in biomarker modeling leveraging the lens of engineering dynamics. Bridges the gap between neuroscience and engineering tools; Reveals space-time dynamics of brain waves via modal analysis and imaging; Addresses nonlinear and stochastic brain wave dynamics.

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