INSIGHT TO MUTUAL INFORMATION AND MATRIX FACTORIZATION WITH LINEAR NEURAL NETWORKS FOR EPILEPSY CLASSIFICATION FROM EEG SIGNALS

As rich spatiotemporal dynamics are exhibited in the human brain, it is quite complicated in nature. Sudden electrical disturbance of the brain occurs in a temporary manner and it causes epileptic seizures. Seizures may be sometimes confused with other events and sometimes it may even go unnoticed. Prediction of occurrence of an epileptic seizure is quite difficult and it is very difficult to understand the course of action. To analyze this widespread disorder of the brain, Electroencephalography (EEG) is used. It is indeed one of the best techniques to probe the activity of the brain and it is highly useful to diagnose the neurological disease. Tons of information is obtained by the EEG monitoring system and analyzing it visually is quite difficult. Therefore, the dimensionality of the EEG data is reduced with the help of dimensionality reduction techniques like Mutual Information (MI) and Matrix Factorization (MF). The values reduced through dimensionality reduction are then classified with the help of Linear Layer Networks for the classification of epilepsy from EEG Signals. Results show that when MI is used to reduce the dimensionality and classified with Linear Layer Networks an average classification accuracy of 96.60% is obtained. When MF is employed with Linear Layer Networks an average classification accuracy of 97.47% is obtained