A different statistical approach aiming at EEG parameter investigation for brain machine interface use

Página do item simplificado
dc.contributor.authorDE CASTRO, M. C. F.
dc.contributor.authorFábio Gerab
dc.contributor.authorOrcidhttps://orcid.org/0000-0001-7869-8048
dc.date.accessioned2022-01-12T22:01:12Z
dc.date.available2022-01-12T22:01:12Z
dc.date.issued2014-03-06
dc.description.abstractA lot of effort has been made to investigate EEG features that could better represent signal characteristics. The results are usually based on the best mean recognition rates and statistical analysis is done only when different methods are compared. In this work, we propose a new approach that applies multiple rate intercomparisons based on large samples aiming at detecting differences among treatments in order to recognize their importance for the classification rates. Ten frequency band compositions expressed by power spectral density averages were extracted from 8 EEG channels during 4 motor imageries, and spatial feature selections were also considered during the recognition process. Classification rate in large samples can be represented by a normal distribution and, for multiple rate inter-comparisons, the level of significance was corrected based on the Bonferroni Method. The variables were considered to be independents and the test was performed as non paired samples in a very conservative approach. The results showed that there are significant differences among cases of spatial feature selection and thus the considered electrodes are important parameters. On the other hand, considering or not the Delta and Theta bands along with different arrangements for Gamma band resulted in no significant difference. Copyright © 2014 SCITEPRESS - Science and Technology Publications. All rights reserved.
dc.description.firstpage244
dc.description.lastpage250
dc.identifier.citationDE CASTRO, M. C. F.; GERAB, F. A different statistical approach aiming at EEG parameter investigation for brain machine interface use. BIOSIGNALS 2014 - 7th Int. Conference on Bio-Inspired Systems and Signal Processing, Proceedings; Part of 7th Int. Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2014, p. 244-250, March, 2014.
dc.identifier.doi10.5220/0004804602440250
dc.identifier.urihttps://repositorio.fei.edu.br/handle/FEI/4052
dc.relation.ispartofBIOSIGNALS 2014 - 7th Int. Conference on Bio-Inspired Systems and Signal Processing, Proceedings; Part of 7th Int. Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2014
dc.rightsAcesso Aberto
dc.subject.otherlanguageEEG
dc.subject.otherlanguageFrequency Bands
dc.subject.otherlanguagePattern Recognition
dc.subject.otherlanguagePower Spectral Density
dc.subject.otherlanguageSpatial Feature Selection
dc.subject.otherlanguageStatistical Analysis
dc.titleA different statistical approach aiming at EEG parameter investigation for brain machine interface use
dc.typeArtigo de evento
fei.scopus.citations0
fei.scopus.eid2-s2.0-84902329644
fei.scopus.subjectBonferroni method
fei.scopus.subjectBrain machine interface
fei.scopus.subjectClassification rates
fei.scopus.subjectConservative approaches
fei.scopus.subjectIntercomparisons
fei.scopus.subjectRecognition process
fei.scopus.subjectSignal characteristic
fei.scopus.subjectStatistical approach
fei.scopus.updated2024-05-01
fei.scopus.urlhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84902329644&origin=inward
Arquivos
Coleções
Artigos