Electroencephalograms (EEGs) are multidimensional spatio-temporal signals that measure brain electrical activity from electrodes placed on the human scalp. EEGs capture changes in brain signals following a shock to a system such as an external stimulus, stroke, or epilepsy. We develop a new statistical approach to study disruptions in the extremes of multi-channel EEG data resulting from the onset of an epileptic seizure. Since a seizure occurs as an abrupt and uncontrolled electrical disturbance, usually reaching extreme picks in the signal scale when compared to normal EEG behavior, one may analyze the data from the vantage point of the Extreme Value Theory. Hence, inspired by the seminal work in Heffernan and Tawn (2004), we investigate changes in the extremal dependence behavior in the network of EEGs following an extreme observation from another channel. To uncover how the seizure affects brain connectivity over time, we add a time-varying structure for the extremal dependence in the H&T model. We use the Penalised Piecewise Constant (PPC) approach of Ross et al. (2018). Our preliminaries results show a different dependence behavior in the pre- and post-seizure moments.