A Modal Approach to the Space-Time Dynamics of Cognitive BiomarkersSystem Identification of Brain Wave Modes Using EEG
A Modal Approach to the Space-Time Dynamics of Cognitive Biomarkers: System Identification of...
Griffith, Tristan D.; Hubbard Jr., James E.; Balas, Mark J.
2023-02-24 00:00:00
[Developing high-fidelity dynamic models of EEG waves often involves complicated nonlinear indices. There is a need to develop modeling techniques that do not make assumptions about signal stationarity while remaining physically interpretable. Such models should allow for traveling wave dynamics and be readily amenable for imaging and classification. This work treats the brain wave dynamical system in canonical state-space terms. A wide class of methods are available for the identification of stochastic systems in state-space terms. Here, several system identification methods are evaluated as a means of modeling brain waves. Strengths and limitations are discussed for each of the methods, and recommendations for specific algorithms are made. In order to remain physically interpretable, these state-space models may be modally decomposed. This view of the EEG dynamics presents the observed EEG data elegantly as a weighted superposition of spatial patterns, each with an associated oscillating frequency. These spatio-temporal modes are readily amenable to analysis. Output-only modal analysis (OMA) and dynamic mode decomposition (DMD) are presented as particularly effective techniques for the identification of dynamic brain wave models.]
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A Modal Approach to the Space-Time Dynamics of Cognitive BiomarkersSystem Identification of Brain Wave Modes Using EEG
[Developing high-fidelity dynamic models of EEG waves often involves complicated nonlinear indices. There is a need to develop modeling techniques that do not make assumptions about signal stationarity while remaining physically interpretable. Such models should allow for traveling wave dynamics and be readily amenable for imaging and classification. This work treats the brain wave dynamical system in canonical state-space terms. A wide class of methods are available for the identification of stochastic systems in state-space terms. Here, several system identification methods are evaluated as a means of modeling brain waves. Strengths and limitations are discussed for each of the methods, and recommendations for specific algorithms are made. In order to remain physically interpretable, these state-space models may be modally decomposed. This view of the EEG dynamics presents the observed EEG data elegantly as a weighted superposition of spatial patterns, each with an associated oscillating frequency. These spatio-temporal modes are readily amenable to analysis. Output-only modal analysis (OMA) and dynamic mode decomposition (DMD) are presented as particularly effective techniques for the identification of dynamic brain wave models.]
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