Research On FMRI Data Analysis And Classification Of Brain Cognitive State

Neuroinformatics combines neuroscience and computational science and informatics. Massive brain images have been produced as new techniques applied. Applying data mining algorithms to analyze brain images and discover brain cognition mechanism has become an important research area.In this paper, we focus on fMRI data preprocessing, feature reduction and extraction, and modeling to classify brain cognitive states without any domain knowledge. fMRI images obtained in scanning experiment can't be used directly, which means image registration and standardization should be accomplished first of all. Preprocessing steps based on Statistical Parametric Mapping are discussed in this paper, and preliminary dimension reduction is achieved after that.After data preprocessing, we use Principal Component Analysis for feature extraction, by which high-dimensional vector can be mapped into low-dimensional latent semantic space. Then, we use rough set theory, in which attribute reduction is an effective method in the calculation of feature selection for principal component selection further; making the principal component data set can adequately describe all the concepts. So, original fMRI data can be effectively represented by several principle components as few as possible, or even just the first principal component. Experimental results show that these this method can reduce vector dimension and extract vector feature without prior knowledge.Based on the above work, we discuss a new model of brain cognitive state classification. After we use principal component analysis and rough set attribute reduction for feature vectors extraction, we consider a new improved quantum particle swarm optimization, which added in the algorithm of the immunity of biomedical, so that quantum particles have a better direction to improve the optimization ability. Experiments on UCI and fMRI brain cognition data classification show that this method of brain-based cognitive state classification of fMRI applications achieved good results.