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Epilepsy is a neurological condition, which affects the nervous system. Automatic seizure detection is very important in clinical practice and has to be achieved by analyzing the electroencephalogram (EEG). Seizures are the clinical manifestations of excessive and hypersynchronous activity of the neurons in the cerebral cortex and represent one of the most frequent malfunctions of the human central nervous system. Therefore, the search for precursors and predictors of a seizure in the human EEG is of utmost clinical relevance and may even lead to a deeper understanding of the seizure generating mechanisms. In this paper, the normal, pre-ictal (background) and ictal (epileptic) EEG signals are studied using higher order spectra. HOS based measures are shown to be able to distinguish epileptic EEG from normal and background EEG with high confident level ( p-value of less than 0.05).

Neural spike train decoding is an important task for understanding how the biological nervous system performs computation and communication. Interest in this field has grown due to recent advances in neural prosthetics, as well as the need to explore non-traditional computational architectures. Current methods deal poorly with or ignore altogether the non-linearities inherent in neural computation. In this paper, we explore polynomial kernel regression as a method for for dealing with neural non-linearities. Two experiments, based on sensory perception and motor control, demonstrate the ability of the approach to decode the neural spike code using synthetic data.