Problem statement: EEG signals during epileptic seizure can be presented as an algebraic structure, namely semigroup of upper triangular matrices. Approach: EEG signals during seizure were recorded and composed into set of matrices. They were transformed into upper triangular matrices using QR-Schur decomposition and finally as a semigroup of upper triangular matrices. Results: EEG signals during epileptic seizure were transformed as a semigroup of upper triangular matrices under matrix multiplication. Conclusion: This study described the procedure to transform signal during epileptic seizure (brainstorm) into an algebraic structure. This is the key step that will enable us to further proceed to obtain some pattern out of the seizure data in our future research

No abstract

Electroencephalography (EEG) signals during epileptic seizure can be viewed as a semigroup of upper triangular matrices under matrix multiplication. In this study, we will provide a novel algebraic structure for EEG signals during epileptic seizure and then find out the group complexity. In this case, the novel structure of EEG signals during seizure is investigated for potential and Average Potential Differences (APD).

No abstract

Electroencephalography (EEG) is a record of electrical activity along the scalp. It is measures voltage fluctuations resulting from ionic current flows within the neurons of the brain. EEG is most often used to diagnose epilepsy, which causes understandable abnormalities in EEG readings. The mathematical analysis of EEG signals assists medical specialists by providing a clarification of the brain activity being observed, so increasing the information about the structure and function of the human brain. EEG signals during epileptic seizure can be viewed as a semigroup of square matrices under matrix multiplication, and every element in that semigroup is shown to be regular. In this paper, we will present some main properties of regular element of EEG signals during epileptic seizure.