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020 _a9783031235290
_9978-3-031-23529-0
024 7 _a10.1007/978-3-031-23529-0
_2doi
050 4 _aR856-857
072 7 _aMQW
_2bicssc
072 7 _aTEC059000
_2bisacsh
072 7 _aMQW
_2thema
082 0 4 _a610.28
_223
100 1 _aGriffith, Tristan D.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_987439
245 1 2 _aA Modal Approach to the Space-Time Dynamics of Cognitive Biomarkers
_h[electronic resource] /
_cby Tristan D. Griffith, James E. Hubbard Jr., Mark J. Balas.
250 _a1st ed. 2023.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2023.
300 _aXIII, 132 p. 40 illus., 31 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSynthesis Lectures on Biomedical Engineering,
_x1930-0336
505 0 _a1. 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.
520 _aThis 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.
650 0 _aBiomedical engineering.
_93292
650 0 _aComputational neuroscience.
_915392
650 0 _aBiochemical markers.
_911965
650 0 _aCognitive neuroscience.
_99262
650 0 _aNeural networks (Computer science) .
_987441
650 1 4 _aBiomedical Engineering and Bioengineering.
_931842
650 2 4 _aComputational Neuroscience.
_915392
650 2 4 _aBiomarkers.
_987443
650 2 4 _aCognitive Neuroscience.
_99262
650 2 4 _aMathematical Models of Cognitive Processes and Neural Networks.
_932913
700 1 _aHubbard Jr., James E.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_987445
700 1 _aBalas, Mark J.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_987446
710 2 _aSpringerLink (Online service)
_987447
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031235283
776 0 8 _iPrinted edition:
_z9783031235306
776 0 8 _iPrinted edition:
_z9783031235313
830 0 _aSynthesis Lectures on Biomedical Engineering,
_x1930-0336
_987448
856 4 0 _uhttps://doi.org/10.1007/978-3-031-23529-0
912 _aZDB-2-SXSC
942 _cEBK
999 _c86099
_d86099