A neural-network technique to learn concepts from electroencephalograms

A new technique is presented developed to learn multi-class concepts from clinical electroencephalograms (EEGs). A desired concept is represented as a neuronal computational model consisting of the input, hidden, and output neurons. In this model the hidden neurons learn independently to classify the EEG segments presented by spectral and statistical… CONTINUE READING