--- abstract: |- 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. altloc: - http://www.ijcai.org/papers07/Papers/IJCAI07-093.pdf chapter: ~ commentary: ~ commref: ~ confdates: 'Jan 6 - 12, 2007' conference: International Joint Conference on Artificial Intelligence confloc: 'Hyderbad, India' contact_email: ~ creators_id: [] creators_name: - family: Singh given: Vishwajeet honourific: '' lineage: '' - family: Miyapuram given: K. P. honourific: '' lineage: '' - family: Bapi given: Raju S. honourific: '' lineage: '' date: 2007 date_type: published datestamp: 2007-01-19 department: ~ dir: disk0/00/00/53/64 edit_lock_since: ~ edit_lock_until: ~ edit_lock_user: ~ editors_id: [] editors_name: [] eprint_status: archive eprintid: 5364 fileinfo: /style/images/fileicons/application_pdf.png;/5364/1/IJCAI07%2D093.pdf full_text_status: public importid: ~ institution: ~ isbn: ~ ispublished: pub issn: ~ item_issues_comment: [] item_issues_count: 0 item_issues_description: [] item_issues_id: [] item_issues_reported_by: [] item_issues_resolved_by: [] item_issues_status: [] item_issues_timestamp: [] item_issues_type: [] keywords: 'Sequence Learning, fMRI, visuomotor, Naive Bayes Classifier, Support Vector machine, Nearest neighbour classification' lastmod: 2011-03-11 08:56:45 latitude: ~ longitude: ~ metadata_visibility: show note: ~ number: ~ pagerange: 587-592 pubdom: TRUE publication: ~ publisher: ~ refereed: TRUE referencetext: | [Ambroise and McLachlan, 2002] C. 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