EEG signal representation of basic geometric bodies

P81: Detection of Epileptic Seizures Using EEG Signal Processing

Epilepsy is the most common brain diseases that cause many problems in the daily life of the patient. In most attempts to automatic detection, the attack used an EEG. In this paper, The complete data set consists of five sets recorded from normal and epileptic patients. Each set containing 100 single-channel EEG segments. Here we used first and last sets (A and E). Set A consisted of segments r...

Basic Principles of Quantitative EEG

The human electroencephalogram (EEG) may be the most complex set of signals in nature and is certainly the most complicated phenomenon routinely subjected to scientific experimentation. That electrical potentials are detectable at the scalp at all is the result of some fortuitous neural architecture. Human neocortex consists of tightly packed arrays of columns, six neurons deep, aligned perpend...

A Time-Frequency approach for EEG signal segmentation

The record of human brain neural activities, namely electroencephalogram (EEG), is generally known as a non-stationary and nonlinear signal. In many applications, it is useful to divide the EEGs into segments within which the signals can be considered stationary. Combination of empirical mode decomposition (EMD) and Hilbert transform, called Hilbert-Huang transform (HHT), is a new and powerful ...

Support Function Representation of Convex Bodies, Its Application in Geometric Computing, and Some Related Representations

The importance of the support function in representation, manipulation, and analysis of convex bodies can indeed be compared with that of the Fourier transform in signal processing. The support function, in intuitive terms, is the signed distance of a supporting plane of a convex body from the origin point. In this paper we show that, just as simple multiplication in the Fourier transform domai...

Basic geometric invariant theory