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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Norman, K.A., Polyn, S.M., Detre1, G.J., Haxby, J.V.: Beyond Mind-reading: Multi-voxel Pattern Analysis of fMRI Data. Trends in Cognitive Sciences 10(9), 424–430 (2006)
Haxby, J.V., Gobbini, M.I., Furey, M.L., Ishai, A., Schouten, J.L., Pietrini, P.: Distributed and Overlapping Representations of Faces and Objects in Ventral Temporal Cortex. Science 293, 2425–2430 (2001)
Kamitani, Y., Tong, F.: Decoding the Subjective Contents of the Human Brain. Nature Neuroscience 8, 679–685 (2005)
Formisano, E., Martino, F.D., Bonte, M., Goebel, R.: Who Is Saying What? Brain-Based Decoding of Human Voice and Speech. Science 322, 970–973 (2008)
Haynes, J.D., Rees, G.: Predicting the Stream of Consciousness from Activity in Human Visual Cortex. Current Biology 15, 1301–1307 (2005)
Mitchell, T.: Learning to Decode Cognitive States from Brain Images. Machine Learning 57, 145–175 (2004)
Mitchell, T., Shinkareva, S.V., Carlson, A., Chang, K.M., Malave, V.L., Mason, R.A., Just, M.A.: Predicting Human Brain Activity Associated with the Meanings of Nouns. Science 320, 1191–1195 (2008)
Pereira, F.: Beyond Brain Blobs: Machine Learning Classifiers as Instruments for Analyzing Functional Magnetic Resonance Imaging Data, vol. 12. School of Computer Science Carnegie Mellon University, Pittsburgh (2007)
Pereira, F., Mitchell, T., Botvinick, M.: Machine Learning Classifiers and fMRI: A tutorial overview. NeuroImage 45, S199–S209 (2009)
Kay, K.N., Naselaris, T., Prenger, R.J., Gallant, J.L.: Identifying Natural Images from Human Brain Activity. Nature 452, 352–355 (2008)
Hasson, U., Nir, Y., Levy, I., Fuhrmann, G., Malach, R.: Intersubject Synchronization of Cortical Activity During Natural Vision. Science 303, 1634–1640 (2004)
Sato, J.R., Fujita, A., Thomaz, C.E., Martin Mda, G., Mourão-Miranda, J., Brammer, M.J., Junior, E.A.: Evaluating SVM and MLDA in the Extraction of Discriminant Regions for Mental State Prediction. NeuroImage 46, 105–114 (2009)
Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines (2001), http://www.csie.ntu.edu.tw/cjlin/libsvm
Qin, Y., Sohn, M.H., Anderson, J.R., Stenger, V.A., Fissell, K., Goode, A., Carter, C.S.: Predicting the Practice Effects on the Blood Oxygenation Level-Dependent (BOLD) Function of fMRI in a Symbolic Manipulation Task. In: Proceeding of the National Academy of Sciences of the United States of America, vol. 100, pp. 4951–4956 (2003)
Anderson, J.R.: How Can the Human Mind Occur in the Physical Universe? Oxford University Press, USA (2007)
Anderson, J.R., Bothell, D., Byrne, M.D., et al.: An Integrated Theory of the Mind. Psychological Review 111(4), 1036–1060 (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Xiang, J., Chen, J., Zhou, H., Qin, Y., Li, K., Zhong, N. (2009). Using SVM to Predict High-Level Cognition from fMRI Data: A Case Study of 4*4 Sudoku Solving. In: Zhong, N., Li, K., Lu, S., Chen, L. (eds) Brain Informatics. BI 2009. Lecture Notes in Computer Science(), vol 5819. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04954-5_27
Download citation
DOI: https://doi.org/10.1007/978-3-642-04954-5_27
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-04953-8
Online ISBN: 978-3-642-04954-5
eBook Packages: Computer ScienceComputer Science (R0)