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Understanding brain dynamics in epilepsy is critical for establishingrigorous control objectives that enable new therapeutic methods to mitigateseizure occurrence. In multichannel electrocorticography (ECoG) recordingsacquired in 21 subjects during a total of 94 seizures, we apply dynamicalsystems stability analysis to assess the balance versus imbalance of seizuredynamics across different timescales and brain regions. Specifically, weconsider a sliding time window multivariate autoregressive linear approximationof the data captured by the ECoG channels, where eigendecomposition of theestimated matrix of coefficients describes the contribution of differentregions to the spatiotemporal process (eigenvectors) associated with aparticular timescale (eigenvalues). Interestingly, we observe a pattern ofeigenvalue evolution and slowly changing (or approximately time-invariant)eigenvectors across both seizures and subjects. The seizure-onset is marked byan increase in high frequency spatial information to which a few regionscontribute for a long period. By contrast, the seizure termination ischaracterized by a sudden, small time period change in dynamics to which manyregions contribute. As the seizure terminates, the relatively stable ictaldynamics rapidly transition into the post-ictal regime, marked by relativelyfast-damping oscillations. Our methodology offers a subject-specificcharacterization of the spatiotemporal behavior of the seizure, providing newinsights into the dynamic patterns and functional interactions between brainregions that occur over different timescales. More generally, our approachinforms the development of engineering objectives that can be used to deploynew control strategies to prevent seizure evolution or to hasten seizuretermination.

Parsing spatiotemporal dynamical stability in ECoG during seizure onset, propagation, and termination