Electroencephalogram signal characteristic extracting method

Abstract

The invention provides an electroencephalogram signal characteristic extracting method. Network average route lengths and clustering coefficients are calculated through wavelet reconstruction, windowing horizontal visibility map complex network conversion and complex network analysis. The average route lengths and clustering coefficients composed of electroencephalogram signals are calculated to achieve characteristic analysis of electroencephalogram signals and chaotic time sequence signals of the electroencephalogram signals of different rhythms. The electroencephalogram signal characteristic extracting method has the advantages that one-dimensional chaotic time sequences are converted into complex networks, according to analysis of network characteristic parameters, fractal characters of the electroencephalogram signals are revealed, the complex non-linearity signals of the electroencephalogram signals are depicted from a brand new angle. The electroencephalogram signal characteristics can be applied to automatic diagnosis of mental disease and a characteristic identifying module of a brain-machine port system. The electroencephalogram signal characteristic extracting method can effectively distinguish the electroencephalogram signals of an epilepsia attach stage and an epilepsia non-attach stage, the equation p<0.1 is met after Mann-Whitney detection, and the electroencephalogram signal characteristic extracting method can be applied to epilepsia electroencephalogram automatic identification.

Description

technical field [0001] The invention belongs to an EEG signal processing method, in particular to an EEG signal feature extraction method. Background technique [0002] The EEG signal is the overall reflection of the electrophysiological activity of brain nerve cells on the surface of the cerebral cortex or scalp, which contains a large amount of physiological and pathological information, and is a nonlinear chaotic time series. EEG signals not only provide the basis for diagnosis and treatment of brain diseases; but also become an important research method for human brain functions such as language, memory, learning and thinking; in terms of engineering applications, brain-computer Interface has also become a research hotspot. EEG signal analysis and feature extraction are important links in diagnostic systems and control systems that provide objective parameters for pattern recognition. [0003] EEG signal feature extraction methods include time-domain methods, frequency...

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Problems solved by technology

[0012] In view of the current situation that the existing EEG signal video domain feature extraction methods cannot reflect the chaotic characteristics of EEG signals and the complex network method is complicated to build a network, the purpose of the present invention is to provide a method for EEG signal feature extraction, by reconstructing wavelet, Windowing and horizontal visualization of complex network conversion and complex network analysis Calculation of network average path length and feature extraction algorithm of clustering coefficients, so as to realize the analysis of chaotic signal network characteristics of EEG signals and EEG signals of different rhythms

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