title: Neuronal spatiotemporal pattern discrimination: the dynamical evolution of seizures.
abstract: We developed a modern numerical approach to the multivariate linear discrimination of Fisher from 1936 based upon singular value decomposition that is sufficiently stable to permit widespread application to spatiotemporal neuronal patterns. We demonstrate this approach on an old problem in neuroscience--whether seizures have distinct dynamical states as they evolve with time. A practical result was the first demonstration that human seizures have distinct initiation and termination dynamics, an important characterization as we seek to better understand how seizures start and stop. Our approach is broadly applicable to a wide variety of neuronal data, from multichannel EEG or MEG, to sequentially acquired optical imaging data or fMRI.
authors.affiliation: Krasnow Institute, Program in Neuroscience, Department of Psychology, George Mason University, Fairfax, VA 22030, USA. sschiff@gmu.edu
authors.forename: Steven J
authors.initials: SJ
authors.lastname: Schiff
authors.forename: Tim
authors.initials: T
authors.lastname: Sauer
authors.forename: Rohit
authors.initials: R
authors.lastname: Kumar
authors.forename: Steven L
authors.initials: SL
authors.lastname: Weinstein
mesh_terms: D000328:Adult; D000465:Algorithms; D004192:Discrimination, Psychological / Q000502:physiology*; D004569:Electroencephalography; D006801:Humans; D016014:Linear Models; D015225:Magnetoencephalography; D015233:Models, Statistical; D010364:Pattern Recognition, Visual / Q000502:physiology*; D012640:Seizures / Q000503:physiopathology*