Abstract
The long-term monitoring of electroencephalogram (EEG) produces a large amount of data that needs to be analyzed We have applied the method of Kohonen's self-organizing feature mapping for classification of epileptic EEG signals to reduce the time needed for the analysis. The results of our experiments show that this new method is suitable for automatic classification of various normal and epileptic signals. The developed system is more sensitive to epileptic seizures than currently available systems.
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