A systematic mapping of feature extraction and feature selection methods of electroencephalogram signals for neurological diseases diagnostic assistance

Authors

Keywords:

Diagnosis, EEG, electroencephalogram, feature extraction, feature selection, neural disease

Abstract

Electroencephalogram (EEG) is a non-invasive tool used to monitor the electrical activities of the brain. EEG signal analysis has several applications in the medical field. It is widely used for clinical diagnostics and for advances in the Brain-Computer Interface (BCI) area. In recent years, several studies about the automatic execution of this analysis have been proposed, motivated by the fact that the visual inspection demands a long time from an expert, besides being subject to a misdiagnosis. In order to extract and select relevant information from recordings of brain electrical activity, many computerized analysis methods have been developed based on different approaches. Although some proposed methods have achieved good performance, many of them are not suitable for real-world application due to their high computational cost. Thus, there is opportunity in the area to identify and categorize techniques in order to support studies about the influence of these techniques on diagnostic performance and to propose the optimization of this task. In this context, this Systematic Mapping evaluates the 144 primary studies identified according to the criteria defined in the protocol. The purpose is to highlight which neurological disorders have been studied in recent years and which techniques for feature extraction and feature selection have been applied during signal analysis, individually or jointly, to provide specifically an automatic diagnosis of a neurological disorder using a classifier.

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Author Biographies

Wallace Faveron de Almeida, Universidade de São Paulo

Wallace Faveron de Almeida Possui graduação em Ciências de Computação pela Universidade de São Paulo (2013). Atualmente é mestrando em Sistemas de Informação pela Universidade de São Paulo. Tem experiência na área de Ciência da Computação, com ênfase em Metodologia e Técnicas da Computação.

Clodoaldo Aparecido de Moraes Lima, Universidade de São Paulo

Clodoaldo Aparecido de Moraes Lima possui graduação em Engenharia Elétrica pela Universidade Federal de Juiz de Fora (1997), mestrado (modalidade Automação), doutorado (modalidade Engenharia de Computação) e pós-doutorado em Engenharia Elétrica pela Universidade Estadual de Campinas (2000), (2004), (2005). Atualmente é professor doutor na Escola de Artes, Ciências e Humanidades da Universidade de São Paulo, atuando como docente-pesquisador no curso de Sistemas de Informação. Desenvolve pesquisas na área de Processamento de Sinais, principalmente sinais biomédicos, Aprendizado de Máquina, com ênfase em métodos de kernel e de comitê de máquinas, Sistemas Biométricos, Modelagem não-paramétrica e análise e Predição de Séries Temporais.

Sarajane Marques Peres, Universidade de São Paulo

Sarajane Marques Peres possui graduação em Ciência da Computação pela Universidade Estadual de Maringá (1996), mestrado em Engenharia de Produção pela Universidade Federal de Santa Catarina (1999) e doutorado em Engenharia Elétrica pela Universidade Estadual de Campinas (2006). Atualmente é professora-pesquisadora associada (livre docente), em regime de dedicação exclusiva, da Universidade de São Paulo, com credenciamento pleno no Programa de Pós Graduação em Sistemas de Informação da USP. Durante agosto de 2018 a dezembro de 2018 atuou como pesquisadora na Vrije Universiteit Amsterdam, nos Países Baixos, e durante janeiro de 2019 a junho de 2019 atuou como pesquisadora na Utrecht University, Países Baixos. Tem experiência na área de Ciência da Computação, com ênfase em Inteligência Computacional. Atualmente está pesquisando na área de Reconhecimento de Padrões aplicado a Análise de Gestos, Mineração de Textos e Mineração de Processos. Autora do livro didático: Introdução à Mineração de Dados: com aplicações em R, publicado como parte da Série Elsevier - SBC.

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Published

2021-06-07

How to Cite

Faveron de Almeida, W., Aparecido de Moraes Lima, C., & Marques Peres, S. (2021). A systematic mapping of feature extraction and feature selection methods of electroencephalogram signals for neurological diseases diagnostic assistance. IEEE Latin America Transactions, 19(5), 735–745. Retrieved from https://latamt.ieeer9.org/index.php/transactions/article/view/3942