Probing cortical excitability using cross-frequency coupling in intracranial EEG recordings: a new method for seizure prediction

The need of a reliable seizure prediction is motivated by the 50 million people in the world suffering from epilepsy, of whom 30% have no control on seizures with current pharmacological treatments. Seizure prediction research holds great promise for such patients, since an effective algorithm will enable the development of a closed-loop system that intervenes before the clinical onset of a seizure. As a step toward practical implementation of this technology, we present a new method based on a measure of brain excitability identified by couplings between low-frequency phases and high-frequency amplitudes of brain oscillations. The proposed method was applied to long-term intracranial recordings of 20 patients with partial epilepsy, for a total of 267 seizures and more than 3400-hour-long interictal activities. We found that our predictor was in 50% of cases better than chance, with an average sensitivity of 98.9% and false prediction rate of 1.84/hour. From these observations, we concluded that our method enables a new quantitative way to identify preictal states with a high risk of seizure generation.