Genertation of Synthetic EEG Signal; Evaluation of Mutual Information and Correlation Coefficient

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Saneesh Cleatus T
Thungamani M


When it comes to the diagnosis and treatment of epilepsy, as well as the general quality of life of the patient, the electroencephalogram (EEG) is an often utilised as auxiliary test to aid in the process. It is the primary diagnostic test for epilepsy because it gives a continuous assessment of brain function with great temporal resolution over a long period of time, making it an excellent tool for early detection of epilepsy. Specifically, in this paper, we propose the creation of two Simulink models that can generate synthetic EEG data while maintaining the statistical characteristics of the EEG. In addition, we present the evaluation of two characteristics such as mutual information and correlation coefficient, in order to test the characteristics of any such synthetic generated data. The characteristics proposed here are tested with standard data available on online repository. Apart from using these characteristics for testing the validity of synthetic data, we may use these characteristics as features for machine learning applications.


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