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Kernel alignment for identifying objective criteria from brain MEG recordings in schizophrenia

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10.1016/j.patrec.2016.07.018
 
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hdl:2117/101758

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Martín Muñoz, MarioMés informacióMés informacióMés informació
Béjar Alonso, JavierMés informacióMés informacióMés informació
Espósito, Gennaro
Catala Roig, NeusMés informacióMés informacióMés informació
Cortés García, Claudio UlisesMés informacióMés informacióMés informació
Viñas, Ferran
Tarragó Bofarull, Josep
Rojo, Emilio
Nowak, Rafal
Document typeArticle
Defense date2016-08-02
PublisherElsevier
Rights accessOpen Access
Attribution-NonCommercial-NoDerivs 3.0 Spain
Except where otherwise noted, content on this work is licensed under a Creative Commons license : Attribution-NonCommercial-NoDerivs 3.0 Spain
ProjectREHABILITACION PERSONALIZADA Y ADAPTATIVA EN TRATAMIENTOS POST-ICTUS: EL I-WALKER (MINECO-TEC2014-56256-C2-2-P)
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.
CitationMartin, 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. 
URIhttp://hdl.handle.net/2117/101758
DOI10.1016/j.patrec.2016.07.018
ISSN0167-8655
Publisher versionhttp://www.sciencedirect.com/science/article/pii/S0167865516301805
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  • Departament de Ciències de la Computació - Articles de revista [996]
  • KEMLG - Grup d'Enginyeria del Coneixement i Aprenentatge Automàtic - Articles de revista [124]
  • GPLN - Grup de Processament del Llenguatge Natural - Articles de revista [97]
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