Multivariate principal oscillation pattern analysis of ICA sources during seizure.
Mapping the dynamics of neural source processes critically involved in initiating and propagating seizure activity is important for effective epilepsy diagnosis, intervention, and treatment. Tracking time-varying shifts in the oscillation modes of an evolving seizure may be useful for both seizure onset detection as well as for improved non-surgical interventions such as microstimulation. In this report we apply a multivariate eigendecomposition method to analyze the time-varying principal oscillation patterns (POPs, or eigenmodes) of maximally-independent (ICA) sources of intracranial EEG data recorded from subdural electrodes implanted in a human patient for evaluation of surgery for epilepsy. Our analysis of a subset of the most dynamically important eigenmodes reveals distinct shifts in characteristic frequency and damping time before, throughout, and following seizures providing insight into the dynamical structure of the system throughout seizure evolution.
Department of Cognitive Science and Swartz Center for Computational Neuroscience (SCCN), Institute for Neural Computation (INC), UCSD. tim@sccn.ucsd.edu
Tim
T
Mullen
Gregory
G
Worrell
Scott
S
Makeig
D000465:Algorithms; D004569:Electroencephalography; D006801:Humans; D012640:Seizures / Q000503:physiopathology*