A Comparison Study of EEG Signals Classifiers for Inter-subject Generalization

Carlos Emiliano Solórzano-Espíndola, Humberto Sossa, Erik Zamora

Resultado de la investigación: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

1 Cita (Scopus)

Resumen

Brain-computer interfaces are a promising technology for applications ranging from rehabilitation to video-games. A common problem for these systems is the ability to classify correctly signals corresponding to different subjects, as a consequence these systems are trained individually for each person. In this paper several classification methods, along with regularization methods, are compared, to establish a baseline for common datasets in the motor imagery paradigm for intra-subject classification and measure how they influence inter-subject classification.

Idioma originalInglés
Título de la publicación alojadaPattern Recognition - 13th Mexican Conference, MCPR 2021, Proceedings
EditoresEdgar Roman-Rangel, Ángel Fernando Kuri-Morales, José Francisco Martínez-Trinidad, Jesús Ariel Carrasco-Ochoa, José Arturo Olvera-López
EditorialSpringer Science and Business Media Deutschland GmbH
Páginas305-315
Número de páginas11
ISBN (versión impresa)9783030770037
DOI
EstadoPublicada - 2021
Evento13th Mexican Conference on Pattern Recognition, MCPR 2021 - Virtual, Online
Duración: 23 jun 202126 jun 2021

Serie de la publicación

NombreLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volumen12725 LNCS
ISSN (versión impresa)0302-9743
ISSN (versión digital)1611-3349

Conferencia

Conferencia13th Mexican Conference on Pattern Recognition, MCPR 2021
CiudadVirtual, Online
Período23/06/2126/06/21

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