Interictal spike analysis using stochastic point process

Interictal spikes are important indicators of epileptic focus (foci). The spiky events in EEG waveforms recorded from different regions of the brain provide important information about the dynamic transitions of epileptic activity. We extract sequences of spikes, called spike trains, from individual EEG channels and evaluate them using the stochastic cross-correlogram, where the time of occurrence of each spike in a single channel is statistically correlated to the times of occurrences of spikes in other channels. A probability density function (pdf) is constructed which provides a distribution of the time intervals between the appearances of spike train events across a pair of channels. The time interval corresponding to the maximum of the pdf indicates a likelihood of latency between the two spiky events during the specified observation period. The higher the peak value, the stronger the cross-correlation between the two spike trains. We have applied the cross-correlogram analysis to subdural EEC data recorded from multiple electrodes spread over different regions of the brain. We calculated the maximum peak interval of the cross-correlogram between every pair of spike trains to observe the dynamical transitions of the interictal activity in the temporal and spatial domain. Our results show that, by inspecting the dynamic developments of spike events in these domains, a powerful tool is obtained for the diagnosis of epileptic focus (foci)