Non polar generalization of flat electroencephalography during epileptic seizure

Abstract

Electroencephalography (EEG) is the recording of brain electric potentials. The EEG works in such a manner that it reads voltage differences on the head, relative to a given point. However, EEG analysis still relies mostly on its visual inspection. Due to the fact that visual inspection is very subjective and hardly allows any statistical analysis or standardization, this study presented a method for mapping EEG signals onto a cube, namely Non-Polar CEEG, which is a mapping between two nontrivial manifolds where the domain is non-polar. By using the mapping, the clustering can be performed on the real time EEG data obtained from the patient who suffers from epileptic seizure. With this technique, information about the dynamics of this recorded EEG signal from a few observations can thus be extracted. Non- Polar CEEG preserves information of recorded EEG signal as well as the location of sensors, free of the 10-20 international system have been designed and mathematically proven. The method also includes revealing the location where the most electrical activities occur in spite of fixed locations of EEG sensors which give a better 3D visualization during seizure attacks. Finally, two major applications of the technique to verify this model are presented, namely the Psychometric-EEG signal identification for epileptic patients, and the Speech-EEG signal analysis for Japanese-Spanish speakers. In Psychometric-EEG signal identification, Non-Polar CEEG successfully identified that most electrical activities in the patient’s brains during a seizure occurred in the same areas of their brains obtained from their psychometric tests. On the other hand, results from Non-Polar CEEG also agreed with Functional Magnetic Resonance Imaging (fMRI) results in localization of the speech area of the brain of Japanese-Spanish speakers. With these, Non-Polar CEEG technique has a great potential and remarkable ability in other fields in particular detecting brain activity non-invasively especially in medical analysis. This powerful and novel method has provided a framework that unifies the great diversity of EEG observations and analysis towards understanding epilepsy disorder and other possibilities