Using SVM to predict high-level cognition from fMRI data: a case study of 4*4 sudoku solving

Authors:
Jie Xiang;Junjie Chen;Haiyan Zhou;Yulin Qin;Kuncheng Li;Ning Zhong
Affiliations:
College of Computer and Software, Taiyuan University of Technology, China and The International WIC Institute, Beijing University of Technology, China;College of Computer and Software, Taiyuan University of Technology, China;The International WIC Institute, Beijing University of Technology, China;The International WIC Institute, Beijing University of Technology, China and Dept of Psychology, Carnegie Mellon University;Dept of Radiology, Xuanwu Hospital, Capital University of Medical Sciences, China;The International WIC Institute, Beijing University of Technology, China and Dept of Life Science and Informatics, Maebashi Institute of Technology, Japan
Venue:
BI'09 Proceedings of the 2009 international conference on Brain informatics
Year:
2009

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Abstract

In this study, we explore the approach using Support Vector Machines (SVM) to predict the high-level cognitive states based on fMRI data. On the base of taking voxels in the brain regions related to problem solving as the features, we compare two feature extraction methods, one is based on the cumulative changes of blood oxygen level dependent (BOLD) signal, and the other is based on the values at each time point in the BOLD signal time course of each trial. We collected the fMRI data while participants were performing a simplified 4*4 Sudoku problems, and predicted the complexity (easy vs. complex) or the steps (1-step vs. 2-steps) of the problem from fMRI data using these two feature extraction methods, respectively. Both methods can produce quite high accuracy, and the performance of the latter method is better than the former. The results indicate that SVM can be used to predict high-level cognitive states from fMRI data. Moreover, the feature extraction based on serial signal change of BOLD effect can predict cognitive states better because it can use abundant and typical information kept in BOLD effect data.