Detection of Cognitive States from fMRI data using Machine Learning Techniques

Singh, Vishwajeet and Miyapuram, K. P. and Bapi, Raju S. (2007) Detection of Cognitive States from fMRI data using Machine Learning Techniques. [Conference Poster]

Full text available as:



Over the past decade functional Magnetic Resonance Imaging (fMRI) has emerged as a powerful technique to locate activity of human brain while engaged in a particular task or cognitive state. We consider the inverse problem of detecting the cognitive state of a human subject based on the fMRI data. We have explored classification techniques such as Gaussian Naive Bayes, k-Nearest Neighbour and Support Vector Machines. In order to reduce the very high dimensional fMRI data, we have used three feature selection strategies. Discriminating features and activity based features were used to select features for the problem of identifying the instantaneous cognitive state given a single fMRI scan and correlation based features were used when fMRI data from a single time interval was given. A case study of visuo-motor sequence learning is presented. The set of cognitive states we are interested in detecting are whether the subject has learnt a sequence, and if the subject is paying attention only towards the position or towards both the color and position of the visual stimuli. We have successfully used correlation based features to detect position-color related cognitive states with 80% accuracy and the cognitive states related to learning with 62.5% accuracy.

Item Type:Conference Poster
Keywords:Sequence Learning, fMRI, visuomotor, Naive Bayes Classifier, Support Vector machine, Nearest neighbour classification
Subjects:Neuroscience > Brain Imaging
Computer Science > Machine Learning
ID Code:5364
Deposited By:Miyapuram, Mr Krishna
Deposited On:19 Jan 2007
Last Modified:11 Mar 2011 08:56

References in Article

Select the SEEK icon to attempt to find the referenced article. If it does not appear to be in cogprints you will be forwarded to the paracite service. Poorly formated references will probably not work.

[Ambroise and McLachlan, 2002] C. Ambroise and G. J. McLachlan. Selection bias in gene extraction on the basis of microarray gene-expression data. Proceedings of the National Academy of Sciences, USA, 99(10):6562–6566, May 2002.

[Friston et al., 1995] K. Friston, J. Ashburner, C. Frith, J. Poline, J. Heather and R. Frackowiak. Spatial registration and normalisation of images. Human Brain Mapping, 2:165–189, 1995.

[Mitchell, 1997] T. Mitchell. Machine Learning. McGraw Hill, 1997.

[Mitchell et al., 2003] T. Mitchell, R. Hutchinson, M. Just, R. Niculescu, F. Pereira and X. Wang. Classifying Instantaneous cognitive states from fMRI data. In American Medical Informatics Association Symposium, 465–469, 2003.

[Mitchell et al., 2004] T. Mitchell, R. Hutchinson, R. Niculescu, F.Pereira, X. Wang, M. Just and S. Newman. Learning to decode cognitive states from brain images. Machine Learning, 57(1-2):145–175, October 2004.

[Miyapuram, 2004] K. P. Miyapuram, Visuomotor Mappings and Sequence Learning: A Whole-Brain fMRI Investigation, Master's thesis, Department of Computer and Information Sciences, University of Hyderabad, India, 2004.

[Nigam et al., 2000] K. Nigam, A. McCallum, S. Thrun and T. Mitchell. Text classification from labeled and unlabeled documents using EM. Machine Learning, 39:103–104, 2000.

[Ogawa et al., 1990] S. Ogawa, T. Lee, A. Kay and D. Tank, Brain magnetic resonance imaging with contrast dependent on blood oxygenation. In Proceedings of the National Academy of Sciences, USA, 87:9868–9872, 1990.

[Singh, 2005] Vishwajeet Singh, Detection of Cognitive States from fMRI data using Machine Learning Techniques, Master’s thesis, Department of Computer and Information Sciences, University of Hyderabad, India, 2005.

[Wang et al., 2004] X. Wang, R. Hutchinson, and T. Mitchell. Training fMRI classifiers to detect cognitive states across multiple human subjects. In Advances in Neural Information Processing Systems, 16:709–716, 2004.

[Yang et al., 2005] K. Yang, H. Yoon and C. Shahabi. A supervised feature subset selection technique for multivariate time series. In Proceedings of the Workshop on Feature Selection for Data Mining: Interfacing Machine Learning with Statistics, 92–101, 2005.

[Yu and Liu, 2003] L. Yu and H. Liu, Feature selection for high-dimensional data: A fast correlation-based filter solution. In Proceedings of The Twentieth International Conference on Machine Leaning, 856–863, 2003.


Repository Staff Only: item control page