Detection of Cognitive States from fMRI Data Using Machine Learning Techniques

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 on...

• Type: Conference
• Year: 2007
• Where: IJCAI
• Authors: Vishwajeet Singh, Krishna P. Miyapuram, Raju S. Bapi
• Keywords: Artificial Intelligence, Cognitive States, Correlation Based Features, Fmri Data, IJCAI 2007