  A new technique is presented developed to learn multi-class concepts from
clinical electroencephalograms. 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
electroencephalogram segments presented by spectral and statistical features.
This technique has been applied to the electroencephalogram data recorded from
65 sleeping healthy newborns in order to learn a brain maturation concept of
newborns aged between 35 and 51 weeks. The 39399 and 19670 segments from these
data have been used for learning and testing the concept, respectively. As a
result, the concept has correctly classified 80.1% of the testing segments or
87.7% of the 65 records.
