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Kernel alignment for identifying objective criteria from brain MEG recordings in schizophrenia
dc.contributor.author | Martín Muñoz, Mario |
dc.contributor.author | Béjar Alonso, Javier |
dc.contributor.author | Espósito, Gennaro |
dc.contributor.author | Catala Roig, Neus |
dc.contributor.author | Cortés García, Claudio Ulises |
dc.contributor.author | Viñas, Ferran |
dc.contributor.author | Tarragó Bofarull, Josep |
dc.contributor.author | Rojo, Emilio |
dc.contributor.author | Nowak, Rafal |
dc.contributor.other | Universitat Politècnica de Catalunya. Departament de Ciències de la Computació |
dc.date.accessioned | 2017-03-01T08:01:37Z |
dc.date.available | 2020-08-01T00:25:34Z |
dc.date.issued | 2016-08-02 |
dc.identifier.citation | Martin, M., Bejar, J., Espósito, G., Català Roig, N., Cortes, C., Viñas, F., Tarragó, J., Rojo, E., Nowak, R. Kernel alignment for identifying objective criteria from brain MEG recordings in schizophrenia. "Pattern recognition letters", 2 Agost 2016. |
dc.identifier.issn | 0167-8655 |
dc.identifier.uri | http://hdl.handle.net/2117/101758 |
dc.description.abstract | The current wide access to data from different neuroimaging techniques has permitted to obtain data to explore the possibility of finding objective criteria that can be used for diagnostic purposes. In order to decide which features of the data are relevant for the diagnostic task, we present in this paper a simple method for feature selection based on kernel alignment with the ideal kernel in support vector machines (SVM). The method presented shows state-of-the-art performance while being more efficient than other methods for feature selection in SVM. It is also less prone to overfitting due to the properties of the alignment measure. All these abilities are essential in neuroimaging study, where the number of features representing recordings is usually very large compared with the number of recordings. The method has been applied to a dataset in order to determine objective criteria for the diagnosis of schizophrenia. The dataset analyzed has been obtained from multichannel magnetoencephalogram (MEG) recordings, corresponding to the recordings during the performance of a mismatch negativity (MMN) auditory task by a set of schizophrenia patients and a control group. All signal frequency bands are analyzed (from d (1–4 Hz) to high frequency ¿ (60–200 Hz)) and the signal correlations among the different sensors for these frequencies are used as features. |
dc.language.iso | eng |
dc.publisher | Elsevier |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/es/ |
dc.subject | Àrees temàtiques de la UPC::Informàtica::Aplicacions de la informàtica::Bioinformàtica |
dc.subject | Àrees temàtiques de la UPC::Ciències de la salut::Salut mental |
dc.subject | Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàtic |
dc.subject.lcsh | Schizophrenia |
dc.subject.lcsh | Machine learning |
dc.subject.lcsh | Kernel functions |
dc.subject.lcsh | Support vector machines |
dc.subject.lcsh | Magnetoencephalography |
dc.subject.other | Machine learning |
dc.subject.other | Feature selection |
dc.subject.other | Kernel methods |
dc.subject.other | MEG |
dc.subject.other | Schizophrenia |
dc.title | Kernel alignment for identifying objective criteria from brain MEG recordings in schizophrenia |
dc.type | Article |
dc.subject.lemac | Cervell -- Processament de dades |
dc.subject.lemac | Esquizofrènia |
dc.subject.lemac | Aprenentatge automàtic |
dc.subject.lemac | Kernel, Funcions de |
dc.subject.lemac | Ecoencefalografia |
dc.contributor.group | Universitat Politècnica de Catalunya. KEMLG - Grup d'Enginyeria del Coneixement i Aprenentatge Automàtic |
dc.contributor.group | Universitat Politècnica de Catalunya. GPLN - Grup de Processament del Llenguatge Natural |
dc.identifier.doi | 10.1016/j.patrec.2016.07.018 |
dc.description.peerreviewed | Peer Reviewed |
dc.relation.publisherversion | http://www.sciencedirect.com/science/article/pii/S0167865516301805 |
dc.rights.access | Open Access |
local.identifier.drac | 18815121 |
dc.description.version | Postprint (author's final draft) |
dc.relation.projectid | info:eu-repo/grantAgreement/MINECO//TEC2014-56256-C2-2-P/ES/REHABILITACION PERSONALIZADA Y ADAPTATIVA EN TRATAMIENTOS POST-ICTUS: EL I-WALKER/ |
local.citation.author | Martin, M.; Bejar, J.; Espósito, G.; Català Roig, N.; Cortes, C.; Viñas, F.; Tarragó, J.; Rojo, E.; Nowak, R. |
local.citation.publicationName | Pattern recognition letters |
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