Classification of Electroencephalography signals using mixture of Features

摘要: Electroencephalography (EEG) signals provide valuable information to study the brain function and neurobiological disorders. Digital signal processing gives important tools for analysis of EEG signals. The primarily focus on classification using different feature extraction methods pattern recognition purpose. various are used extracting relevant from data is Discrete Wavelet Transform (DWT), Spectral Autoregressive (AR) Model Lyapunov Exponents. was collected standard repository source. two classifiers ANN CNN A technique proposed based combined features extracted methods. In committed neural network, several independent networks trained by constituted a committee. This committee takes final decisions which in turn represents response individual networks. performance algorithm evaluated 300 recordings three cases comprising healthy volunteers with eyes open, epilepsy patients epileptogenic zone during seizure-free interval, epileptic seizures. experimental results show that higher than some earlier established techniques.