Detection And Analysis Software Of Cognitive EEG Research

Human brain is the most important and the most complex physical organ. It is the basic material of human thinking and the center of neural activity. EEG is reflected electrophysiological activity of brain cells in the cerebral cortex or the whole surface of the scalp. EEG contains a large number of physiological and disease information. In clinical medicine, EEG can not only provide for a certain brain disease diagnosis, but also for a certain brain diseases provide an effective means of treatment.In recent years, cognitive science has been widely attention to how effective analysis and characterization of cognitive function, and has great significance to cognitive disease detection and treatment. At present the evaluation of cognitive function is commonly used neuropsychological testing, conventional EEG and other methods, however their evaluation of cognitive function in patients with dementia have a certain limitations. Based on the existing study of EEG analysis methods and characteristics signal of cognitive extraction methods, this paper selected the power spectrum analysis, wavelet transform, consistency detection, and other time-domain,frequency, time-frequency detection analysis, EEG cognitive features of the extraction of information.At the same time, the EEG signal is chaotic, which has a wide range of features of nonlinear dynamics. This point of view is confirmed and recognized by academic community. It can be applied to nonlinear dynamics of EEG, extract quickly and efficiently the nonlinear dynamics EEG parameters. At present, many studies have explored through these parameters and cognitive function, has studied this issue by using nonlinear dynamics approach to the analysis of the EEG. This paper also used the method of nonlinear dynamics analysis.In this paper, the integrated development of a variety of time domain, frequency domain, time-frequency detection analysis, nonlinear dynamic analysis of the EEG analysis software systems, mainly used to describe the cognitive function, can provide more accurate and sufficient data for researchers. For the convenience of users, the system also provides a wide range of cognitive signals in the characterization parameters, and provides the storage, printing, statistics, and other functions for several metrical results.