Research And Application Of Brain Cognitive States Classification And Recognition Method

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 hidden 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.Two kinds of data-driven feature extraction algorithms are proposed:Latent Semantic Analysis is based on Single Value Decomposition, by which high-dimensional vector can be mapped into low-dimensional latent semantic space. Using LSA, not only an effective dimension reduction can be achieved, but also the potential information of fMRI data can be captured. Principal Component Analysis is based on Eigenvalue Decomposition, by which the correlation among multiple variables can be eliminated. Using PCA, 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 two methods can reduce vector dimension and extract vector feature without prior knowledge.Two kinds of brain cognitive states classification model are introduced based on the above work. After feature extraction by LSA and PCA, based on similarity, samples to be treated are classified by an improved K-nearest neighbor algorithm. Considering the time series characteristics of fMRI data, a swarm-based Hidden Markov Model is proposed. After extracting the first principal component of each fMRI image by PCA, the improved HMM is applied in fMRI time series classification. Experimental results indicate that these two methods performance very well in brain cognitive states classification and recognition.