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

Carlos Emiliano Solórzano-Espíndola, Humberto Sossa, Erik Zamora

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

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.

Original languageEnglish
Title of host publicationPattern Recognition - 13th Mexican Conference, MCPR 2021, Proceedings
EditorsEdgar Roman-Rangel, Ángel Fernando Kuri-Morales, José Francisco Martínez-Trinidad, Jesús Ariel Carrasco-Ochoa, José Arturo Olvera-López
PublisherSpringer Science and Business Media Deutschland GmbH
Pages305-315
Number of pages11
ISBN (Print)9783030770037
DOIs
StatePublished - 2021
Event13th Mexican Conference on Pattern Recognition, MCPR 2021 - Virtual, Online
Duration: 23 Jun 202126 Jun 2021

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12725 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference13th Mexican Conference on Pattern Recognition, MCPR 2021
CityVirtual, Online
Period23/06/2126/06/21

Keywords

  • BCI
  • Classification
  • EEG
  • Inter-subject
  • Motor imagery

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