Channel capacity in brain-computer interfaces.

Registro completo de metadados
MetadadosDescriçãoIdioma
Autor(es): dc.creatorCosta, Thiago Bulhões da Silva-
Autor(es): dc.creatorSuarez Uribe, Luisa Fernanda-
Autor(es): dc.creatorCarvalho, Sarah Negreiros de-
Autor(es): dc.creatorSoriano, Diogo Coutinho-
Autor(es): dc.creatorCastellano, Gabriela-
Autor(es): dc.creatorSuyama, Ricardo-
Autor(es): dc.creatorAttux, Romis Ribeiro de Faissol-
Autor(es): dc.creatorPanazio, Cristiano Magalhães-
Data de aceite: dc.date.accessioned2022-02-21T19:58:37Z-
Data de disponibilização: dc.date.available2022-02-21T19:58:37Z-
Data de envio: dc.date.issued2020-10-29-
Data de envio: dc.date.issued2020-10-29-
Data de envio: dc.date.issued2019-
Fonte completa do material: dc.identifierhttp://www.repositorio.ufop.br/handle/123456789/12908-
Fonte completa do material: dc.identifierhttps://iopscience.iop.org/article/10.1088/1741-2552/ab6cb7-
Fonte completa do material: dc.identifierhttps://doi.org/10.1088/1741-2552/ab6cb7-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/capes/650533-
Descrição: dc.descriptionObjective. Adapted from the concept of channel capacity, the information transfer rate (ITR) has been widely used to evaluate the performance of a brain–computer interface (BCI). However, its traditional formula considers the model of a discrete memoryless channel in which the transition matrix presents very particular symmetries. As an alternative to compute the ITR, this work indicates a more general closed-form expression—also based on that channel model, but with less restrictive assumptions—and, with the aid of a selection heuristic based on a wrapper algorithm, extends such formula to detect classes that deteriorate the operation of a BCI system. Approach. The benchmark is a steady-state visually evoked potential (SSVEP)-based BCI dataset with 40 frequencies/classes, in which two scenarios are tested: (1) our proposed formula is used and the classes are gradually evaluated in the order of the class labels provided with the dataset; and (2) the same formula is used but with the classes evaluated progressively by a wrapper algorithm. In both scenarios, the canonical correlation analysis (CCA) is the tool to detect SSVEPs. Main results. Before and after class selection using this alternative ITR, the average capacity among all subjects goes from 3.71 1.68 to 4.79 0.70 bits per symbol, with p -value  <0.01, and, for a supposedly BCI-illiterate subject, her/his capacity goes from 1.53 to 3.90 bits per symbol. Significance. Besides indicating a consistent formula to compute ITR, this work provides an efficient method to perform channel assessment in the context of a BCI experiment and argues that such method can be used to study BCI illiteracy.-
Formato: dc.formatapplication/pdf-
Idioma: dc.languageen-
Direitos: dc.rightsrestrito-
Palavras-chave: dc.subjectInformation transfer rate-
Título: dc.titleChannel capacity in brain-computer interfaces.-
Aparece nas coleções:Repositório Institucional - UFOP

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