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]

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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

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