Comput Math Methods Med - Mixed-norm regularization for brain decoding.

Tópicos

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Resumo

This work investigates the use of mixed-norm regularization for sensor selection in event-related potential (ERP) based brain-computer interfaces (BCI). The classification problem is cast as a discriminative optimization framework where sensor selection is induced through the use of mixed-norms. This framework is extended to the multitask learning situation where several similar classification tasks related to different subjects are learned simultaneously. In this case, multitask learning helps in leveraging data scarcity issue yielding to more robust classifiers. For this purpose, we have introduced a regularizer that induces both sensor selection and classifier similarities. The different regularization approaches are compared on three ERP datasets showing the interest of mixed-norm regularization in terms of sensor selection. The multitask approaches are evaluated when a small number of learning examples are available yielding to significant performance improvements especially for subjects performing poorly.

Resumo Limpo

work investig use mixednorm regular sensor select eventrel potenti erp base braincomput interfac bci classif problem cast discrimin optim framework sensor select induc use mixednorm framework extend multitask learn situat sever similar classif task relat differ subject learn simultan case multitask learn help leverag data scarciti issu yield robust classifi purpos introduc regular induc sensor select classifi similar differ regular approach compar three erp dataset show interest mixednorm regular term sensor select multitask approach evalu small number learn exampl avail yield signific perform improv especi subject perform poor

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