NDLI: Brain Activation Detection by Neighborhood One-Class SVM

Content Provider	IEEE Xplore Digital Library
Author	Jian Yang Ning Zhong Peipeng Liang Jue Wang Yiyu Yao Shengfu Lu
Copyright Year	2007
Description	Author affiliation: Beijing Univ. of Technol., Beijing (Jian Yang)
Abstract	Brain activation detection is an important problem in fMRI data analysis. In this paper, we propose a data-driven activation detection method called neighborhood one-class SVM (NOC-SVM). By incorporating the idea of neighborhood consistency into one-class SVM, the method classifies a voxel as an activated or non-activated voxel by its neighbor weighted distance to a hyperplane in a high- dimensional kernel space. On two synthetic datasets under different SNRs, the proposed method almost has lower error rate than K-means clustering and fuzzy K-means clustering. On a real fMRI dataset, all the three algorithms can detect similar activated regions. Furthermore, the NOC-SVM is more stable than random algorithms, such as K-means clustering and fuzzy K-means clustering. These results show that the proposed NOC-SVM is a new effective method for activation detections in fMRI data.
Starting Page	47
Ending Page	51
File Size	617209
Page Count	5
File Format	PDF
ISBN	0769530281
DOI	10.1109/WI-IATW.2007.11
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-11-05
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Computer science Data analysis Error analysis Clustering algorithms Support vector machine classification Independent component analysis Kernel Informatics fMRIactivation detectionone-classSVM Intelligent agent
Content Type	Text
Resource Type	Article

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