---
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
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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: ~
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refereed: TRUE
referencetext: |
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[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.
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relation_type: []
relation_uri: []
reportno: ~
rev_number: 12
series: ~
source: ~
status_changed: 2007-09-12 17:09:22
subjects:
- brain-img
- comp-sci-mach-learn
succeeds: ~
suggestions: ~
sword_depositor: ~
sword_slug: ~
thesistype: ~
title: Detection of Cognitive States from fMRI data using Machine Learning Techniques
type: confposter
userid: 6034
volume: ~