Neural Codes in Human Extracranial EEG: Identification of Epilepsy Features
Taufik Valiante1, Alan L Chiu, Berj Bardakjian
1Department of Surgery, Toronto Western Research Institute, University of Toronto, Toronto, Canada.
Abstract:
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. The accuracy of state classification was improved when more training data was used. The corresponding changes in synaptic weights between artificial neural units associated with more training data was studied to determine the correlations between learning in WANN and frequency information in human epilepsy.
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