@inproceedings{
 title = {fMRI activated-voxel detection based on ICA decomposition and wavelet analysis},
 type = {inproceedings},
 year = {2010},
 websites = {http://www.cic.ipn.mx/~pescamilla/papers/Morales-Floresetal2010.pdf},
 id = {64a9a473-a9a8-3f2f-9bd9-9ad617e2ae5a},
 created = {2022-08-29T17:43:15.680Z},
 file_attached = {false},
 profile_id = {940dd160-7d67-3a5f-b9f8-935da0571367},
 group_id = {92fccab2-8d44-33bc-b301-7b94bb18523c},
 last_modified = {2022-08-29T17:43:15.680Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 source_type = {CONF},
 private_publication = {false},
 abstract = {Functional magnetic resonance imaging (fMRI) is a technique for investigating activity in the brain in response to some applied stimulus during a mental process. In this paper, detection of activated voxels in a two-class block-design fMRI experiment is described. A methodology based on blind source separation using Independent Component Analysis (ICA), applied on wavelet decomposition of voxel time signals is shown to provide an adequate and robust data separation. Results obtained from simulated as well as experimental fMRI data obtained from the public repository of the fMRI Data Center, are presented.},
 bibtype = {inproceedings},
 author = {Morales-Flores, E and Ramirez-Cortes, J M and Escamilla-Ambrosio, P J and Alarcon-Aquino, V},
 booktitle = {ROPEC 2010}
}

{"_id":"TpNrP3HD9Ggbm4t3a","bibbaseid":"moralesflores-ramirezcortes-escamillaambrosio-alarconaquino-fmriactivatedvoxeldetectionbasedonicadecompositionandwaveletanalysis-2010","downloads":0,"creationDate":"2018-11-16T03:23:24.343Z","title":"fMRI activated-voxel detection based on ICA decomposition and wavelet analysis","author_short":["Morales-Flores, E.","Ramirez-Cortes, J., M.","Escamilla-Ambrosio, P., J.","Alarcon-Aquino, V."],"year":2010,"bibtype":"inproceedings","biburl":"https://bibbase.org/service/mendeley/940dd160-7d67-3a5f-b9f8-935da0571367","bibdata":{"title":"fMRI activated-voxel detection based on ICA decomposition and wavelet analysis","type":"inproceedings","year":"2010","websites":"http://www.cic.ipn.mx/~pescamilla/papers/Morales-Floresetal2010.pdf","id":"64a9a473-a9a8-3f2f-9bd9-9ad617e2ae5a","created":"2022-08-29T17:43:15.680Z","file_attached":false,"profile_id":"940dd160-7d67-3a5f-b9f8-935da0571367","group_id":"92fccab2-8d44-33bc-b301-7b94bb18523c","last_modified":"2022-08-29T17:43:15.680Z","read":false,"starred":false,"authored":false,"confirmed":"true","hidden":false,"source_type":"CONF","private_publication":false,"abstract":"Functional magnetic resonance imaging (fMRI) is a technique for investigating activity in the brain in response to some applied stimulus during a mental process. In this paper, detection of activated voxels in a two-class block-design fMRI experiment is described. A methodology based on blind source separation using Independent Component Analysis (ICA), applied on wavelet decomposition of voxel time signals is shown to provide an adequate and robust data separation. Results obtained from simulated as well as experimental fMRI data obtained from the public repository of the fMRI Data Center, are presented.","bibtype":"inproceedings","author":"Morales-Flores, E and Ramirez-Cortes, J M and Escamilla-Ambrosio, P J and Alarcon-Aquino, V","booktitle":"ROPEC 2010","bibtex":"@inproceedings{\n title = {fMRI activated-voxel detection based on ICA decomposition and wavelet analysis},\n type = {inproceedings},\n year = {2010},\n websites = {http://www.cic.ipn.mx/~pescamilla/papers/Morales-Floresetal2010.pdf},\n id = {64a9a473-a9a8-3f2f-9bd9-9ad617e2ae5a},\n created = {2022-08-29T17:43:15.680Z},\n file_attached = {false},\n profile_id = {940dd160-7d67-3a5f-b9f8-935da0571367},\n group_id = {92fccab2-8d44-33bc-b301-7b94bb18523c},\n last_modified = {2022-08-29T17:43:15.680Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n source_type = {CONF},\n private_publication = {false},\n abstract = {Functional magnetic resonance imaging (fMRI) is a technique for investigating activity in the brain in response to some applied stimulus during a mental process. In this paper, detection of activated voxels in a two-class block-design fMRI experiment is described. A methodology based on blind source separation using Independent Component Analysis (ICA), applied on wavelet decomposition of voxel time signals is shown to provide an adequate and robust data separation. Results obtained from simulated as well as experimental fMRI data obtained from the public repository of the fMRI Data Center, are presented.},\n bibtype = {inproceedings},\n author = {Morales-Flores, E and Ramirez-Cortes, J M and Escamilla-Ambrosio, P J and Alarcon-Aquino, V},\n booktitle = {ROPEC 2010}\n}","author_short":["Morales-Flores, E.","Ramirez-Cortes, J., M.","Escamilla-Ambrosio, P., J.","Alarcon-Aquino, V."],"urls":{"Website":"http://www.cic.ipn.mx/~pescamilla/papers/Morales-Floresetal2010.pdf"},"biburl":"https://bibbase.org/service/mendeley/940dd160-7d67-3a5f-b9f8-935da0571367","bibbaseid":"moralesflores-ramirezcortes-escamillaambrosio-alarconaquino-fmriactivatedvoxeldetectionbasedonicadecompositionandwaveletanalysis-2010","role":"author","metadata":{"authorlinks":{}},"downloads":0},"search_terms":["fmri","activated","voxel","detection","based","ica","decomposition","wavelet","analysis","morales-flores","ramirez-cortes","escamilla-ambrosio","alarcon-aquino"],"keywords":[],"authorIDs":[],"dataSources":["uTykyhFv6T7J2dX6z","ya2CyA73rpZseyrZ8","nyWSrx2yzJEsoe49A","2252seNhipfTmjEBQ"]}