Neural Codes in Human Extracranial EEG: Identification of Epilepsy Features.

Features of epilepsy from human extracranial EEG recordings were obtained using the wavelet artificial neural network (WANN). The WANN is also a robust signal processing tool for the estimation of nonlinear time-frequency relation and it had previously been shown to be able to classify and predict state transitions in the in-vitro hippocampal slice model exhibiting spontaneous epilepsy. The variations in the power-frequency spectrum were analyzed.

Prevention of Spontaneous Seizure-like Events in Both in-silico and in-vitro Epilepsy Models.

In this paper, we apply the small perturbation control strategy for the prevention of seizure-like events (SLEs) characterized as lower dimensional possibly rhythmic (LPR) activities in both the coupled oscillators in-silico model and the in-vitro low magnesium rat hippocampal slice model. Utilizing the wavelet artificial neural network (WANN), state transitions towards SLEs can be predicted. Successful suppression of SLEs was achieved when brief control perturbations were applied to the field coupling portals of the coupled oscillators model and to the mossy fibers via extracellular field stimulating electrode, respectively.