Study On Recognition And Classification Of Trait Anxiety EEG

Anxiety is an emotional response, is familiar with a negative emotional state, and trait anxiety is one of Anxiety, which refers to a general personality characteristics or traits. Studies have shown that the different state of mind of emotions and changes in the cerebral cortex in different locations will reflect different brain signals. A number of frequency components are included in spontaneous EEG and the energy corresponding to different frequency bands, which is detected in different physiological states and cognitive tasks, changes with the scalp area. Thus the energy corresponding to a certain frequency sub-band can be taken as a feature parameter of the classifier to realize the Classification of Trait anxiety EEG. In this paper, independent component analysis (ICA)was used to preprocess the raw EEG, then the use of wavelet analysis was supposed for the extraction of discriminating features from trait anxiety and non-trait anxiety’s EEG. Subsequently, combined with support eigenvector machine classifier (SVM), the paper achieved the classification of two types of signal pattern. The main work done in this dissertation is as follows:1. The use of independent component analysis (ICA) to preprocess the original signal. Artifact in the EEG can be considered by the independent source generated, and each source can be considered as a mixture of Linear mixed, so ICA was applied to Trait anxiety EEG analysis and preprocess.2. Using the method of wavelet transaction to decompose the EEG recordings into various frequency bands through multi-scale decomposition, then using wavelet coefficients to extract the energy feature that will be as the classifier input vector. Multi-scale wavelet transform is a signal analysis method, which has good time-frequency localization properties. It is ideal for the analysis of non-stationary signals such as transient and time-varying characteristics.3. The use of support to the machine (SVM) to extract the feature eigenvector for training and testing, to achieve the Trait anxiety EEG recognition and classification. SVM is a machine to solve the small sample, nonlinear and high dimensional problems, which shows unique advantages in machine learning. Using SVM for classification and identification of EEG as well as brain function research is extremely important and valuable.Experimental results show that the feature parameters extracted by wavelet analysis, as the SVM input eigenvector can achieve relatively good results, classification accuracy arrives86%.