Brain fMRI processing and classification based on combination of PCA and SVM

  • Authors:

  • Song-yun Xie;Rang Guo;Ning-fei Li;Ge Wang;Hai-tao Zhao

  • Affiliations:

  • Department of Electronic and Information, Northwestern Polytechnical University, Xi'an, China;Department of Electronic and Information, Northwestern Polytechnical University, Xi'an, China;Department of Electronic and Information, Northwestern Polytechnical University, Xi'an, China;Department of Electronic and Information, Northwestern Polytechnical University, Xi'an, China;First Accessorial Hospital, Forth Military Medical University, Xi'an, China

  • Venue:
  • IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
  • Year:
  • 2009

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Abstract

fMRI is one of the fundamental tools for functional human brain research. However, fMRI data are often in a high dimensional feature space and suffer greatly from large and complex dataset. To relieve the curse of dimensionality in fMRI image, PCA combines with SVM to form a feature-based classification method in this work. PCA is employed to find a more compact and reasonable representation of the data by extracting features from each fMRI image. Then a linear kernel SVM classifier is trained on the selected features to detect different brain states. The advantage of incorporating PCA with SVM is twofold: Firstly, the computational burden on SVM classifier is reduced significantly. Secondly, a less complex classifier is well established. Experimental results show that the proposed method yields good performance. The correct rate of our hand-movement fMRI study with both healthy subjects and a tumor patient verified the stability and generallzation capability of the method.