An evolutionary computation approach to cognitive states classification

The study of human brain functions has dramatically increased in recent years greatly due to the advent of functional magnetic resonance imaging. This paper presents a genetic programming approach to the problem of classifying the instantaneous cognitive state of a person based on his/her functional...

Full description

Saved in:
Bibliographic Details
Main Authors: Ramirez, R., Puiggros, M.
Format: Conference Proceeding
Language:English
Subjects:
Automatic testing
Blood flow
Brain
Communications technology
Evolutionary computation
Genetic programming
Humans
Magnetic noise
Magnetic resonance
Magnetic resonance imaging
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
cited_by
cites
container_end_page 1799
container_issue
container_start_page 1793
container_title
container_volume
creator Ramirez, R.
Puiggros, M.
description The study of human brain functions has dramatically increased in recent years greatly due to the advent of functional magnetic resonance imaging. This paper presents a genetic programming approach to the problem of classifying the instantaneous cognitive state of a person based on his/her functional magnetic resonance imaging data. The problem provides a very interesting case study of training classifiers with extremely high dimensional, sparse and noisy data. We apply genetic programming for both feature selection and classifier training. We present a successful case study of induced classifiers which accurately discriminate between cognitive states produced by listening to different auditory stimuli.
doi_str_mv 10.1109/CEC.2007.4424690
format conference_proceeding
fullrecord <record><control><sourceid>ieee_6IE</sourceid><recordid>TN_cdi_ieee_primary_4424690</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>4424690</ieee_id><sourcerecordid>4424690</sourcerecordid><originalsourceid>FETCH-LOGICAL-i175t-c0c3607775d30a43315c8d9954838898a315caf2de395fe3af2070c8821ec9813</originalsourceid><addsrcrecordid>eNo9kE9PwzAMxcM_iTF2R-KSL9Bi10mTHKdqDKRJXEDiNkVpCkFdWzXdJL49GUz44uf3s54sM3aHkCOCeahWVV4AqFyIQpQGztgNJiWQBMA5m6ERmAEU5cU_IKMuEwBtMqX0-zVbxPgFqYQUqMSMrZcd94e-3U-h7-z4zV2_G_aTPY7cDsPYW_fJpz75H12YwsHzmKiP3LU2xtAE97t7y64a20a_OPU5e3tcvVZP2eZl_VwtN1lAJafMgaMSlFKyJrCCCKXTtTFSaNLaaHs0bFPUnoxsPCUJCpzWBXpnNNKc3f_lBu_9dhjDLh29PT2EfgDiwU_1</addsrcrecordid><sourcetype>Publisher</sourcetype><iscdi>true</iscdi><recordtype>conference_proceeding</recordtype></control><display><type>conference_proceeding</type><title>An evolutionary computation approach to cognitive states classification</title><source>IEEE Electronic Library (IEL) Conference Proceedings</source><creator>Ramirez, R. ; Puiggros, M.</creator><creatorcontrib>Ramirez, R. ; Puiggros, M.</creatorcontrib><description>The study of human brain functions has dramatically increased in recent years greatly due to the advent of functional magnetic resonance imaging. This paper presents a genetic programming approach to the problem of classifying the instantaneous cognitive state of a person based on his/her functional magnetic resonance imaging data. The problem provides a very interesting case study of training classifiers with extremely high dimensional, sparse and noisy data. We apply genetic programming for both feature selection and classifier training. We present a successful case study of induced classifiers which accurately discriminate between cognitive states produced by listening to different auditory stimuli.</description><identifier>ISSN: 1089-778X</identifier><identifier>ISBN: 1424413397</identifier><identifier>ISBN: 9781424413393</identifier><identifier>EISSN: 1941-0026</identifier><identifier>EISBN: 1424413400</identifier><identifier>EISBN: 9781424413409</identifier><identifier>DOI: 10.1109/CEC.2007.4424690</identifier><language>eng</language><publisher>IEEE</publisher><subject>Automatic testing ; Blood flow ; Brain ; Communications technology ; Evolutionary computation ; Genetic programming ; Humans ; Magnetic noise ; Magnetic resonance ; Magnetic resonance imaging</subject><ispartof>2007 IEEE Congress on Evolutionary Computation, 2007, p.1793-1799</ispartof><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/4424690$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,780,784,789,790,2058,27925,54555,54796,54920,54932</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/4424690$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Ramirez, R.</creatorcontrib><creatorcontrib>Puiggros, M.</creatorcontrib><title>An evolutionary computation approach to cognitive states classification</title><title>2007 IEEE Congress on Evolutionary Computation</title><addtitle>CEC</addtitle><description>The study of human brain functions has dramatically increased in recent years greatly due to the advent of functional magnetic resonance imaging. This paper presents a genetic programming approach to the problem of classifying the instantaneous cognitive state of a person based on his/her functional magnetic resonance imaging data. The problem provides a very interesting case study of training classifiers with extremely high dimensional, sparse and noisy data. We apply genetic programming for both feature selection and classifier training. We present a successful case study of induced classifiers which accurately discriminate between cognitive states produced by listening to different auditory stimuli.</description><subject>Automatic testing</subject><subject>Blood flow</subject><subject>Brain</subject><subject>Communications technology</subject><subject>Evolutionary computation</subject><subject>Genetic programming</subject><subject>Humans</subject><subject>Magnetic noise</subject><subject>Magnetic resonance</subject><subject>Magnetic resonance imaging</subject><issn>1089-778X</issn><issn>1941-0026</issn><isbn>1424413397</isbn><isbn>9781424413393</isbn><isbn>1424413400</isbn><isbn>9781424413409</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2007</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><recordid>eNo9kE9PwzAMxcM_iTF2R-KSL9Bi10mTHKdqDKRJXEDiNkVpCkFdWzXdJL49GUz44uf3s54sM3aHkCOCeahWVV4AqFyIQpQGztgNJiWQBMA5m6ERmAEU5cU_IKMuEwBtMqX0-zVbxPgFqYQUqMSMrZcd94e-3U-h7-z4zV2_G_aTPY7cDsPYW_fJpz75H12YwsHzmKiP3LU2xtAE97t7y64a20a_OPU5e3tcvVZP2eZl_VwtN1lAJafMgaMSlFKyJrCCCKXTtTFSaNLaaHs0bFPUnoxsPCUJCpzWBXpnNNKc3f_lBu_9dhjDLh29PT2EfgDiwU_1</recordid><startdate>200709</startdate><enddate>200709</enddate><creator>Ramirez, R.</creator><creator>Puiggros, M.</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>200709</creationdate><title>An evolutionary computation approach to cognitive states classification</title><author>Ramirez, R. ; Puiggros, M.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i175t-c0c3607775d30a43315c8d9954838898a315caf2de395fe3af2070c8821ec9813</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2007</creationdate><topic>Automatic testing</topic><topic>Blood flow</topic><topic>Brain</topic><topic>Communications technology</topic><topic>Evolutionary computation</topic><topic>Genetic programming</topic><topic>Humans</topic><topic>Magnetic noise</topic><topic>Magnetic resonance</topic><topic>Magnetic resonance imaging</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Ramirez, R.</creatorcontrib><creatorcontrib>Puiggros, M.</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Xplore</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Ramirez, R.</au><au>Puiggros, M.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>An evolutionary computation approach to cognitive states classification</atitle><btitle>2007 IEEE Congress on Evolutionary Computation</btitle><stitle>CEC</stitle><date>2007-09</date><risdate>2007</risdate><spage>1793</spage><epage>1799</epage><pages>1793-1799</pages><issn>1089-778X</issn><eissn>1941-0026</eissn><isbn>1424413397</isbn><isbn>9781424413393</isbn><eisbn>1424413400</eisbn><eisbn>9781424413409</eisbn><abstract>The study of human brain functions has dramatically increased in recent years greatly due to the advent of functional magnetic resonance imaging. This paper presents a genetic programming approach to the problem of classifying the instantaneous cognitive state of a person based on his/her functional magnetic resonance imaging data. The problem provides a very interesting case study of training classifiers with extremely high dimensional, sparse and noisy data. We apply genetic programming for both feature selection and classifier training. We present a successful case study of induced classifiers which accurately discriminate between cognitive states produced by listening to different auditory stimuli.</abstract><pub>IEEE</pub><doi>10.1109/CEC.2007.4424690</doi><tpages>7</tpages></addata></record>
fulltext fulltext_linktorsrc
identifier ISSN: 1089-778X
ispartof 2007 IEEE Congress on Evolutionary Computation, 2007, p.1793-1799
issn 1089-778X
1941-0026
language eng
recordid cdi_ieee_primary_4424690
source IEEE Electronic Library (IEL) Conference Proceedings
subjects Automatic testing
Blood flow
Brain
Communications technology
Evolutionary computation
Genetic programming
Humans
Magnetic noise
Magnetic resonance
Magnetic resonance imaging
title An evolutionary computation approach to cognitive states classification
url http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-27T21%3A12%3A00IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-ieee_6IE&rft_val_fmt=info:ofi/fmt:kev:mtx:book&rft.genre=proceeding&rft.atitle=An%20evolutionary%20computation%20approach%20to%20cognitive%20states%20classification&rft.btitle=2007%20IEEE%20Congress%20on%20Evolutionary%20Computation&rft.au=Ramirez,%20R.&rft.date=2007-09&rft.spage=1793&rft.epage=1799&rft.pages=1793-1799&rft.issn=1089-778X&rft.eissn=1941-0026&rft.isbn=1424413397&rft.isbn_list=9781424413393&rft_id=info:doi/10.1109/CEC.2007.4424690&rft.eisbn=1424413400&rft.eisbn_list=9781424413409&rft_dat=%3Cieee_6IE%3E4424690%3C/ieee_6IE%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-i175t-c0c3607775d30a43315c8d9954838898a315caf2de395fe3af2070c8821ec9813%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_id=info:pmid/&rft_ieee_id=4424690&rfr_iscdi=true