Can graph metrics be used for EEG-BCIs based on hand motor imagery?

Autor(es)

Stefano Filho, Carlos Alberto ^{[1, 2]} ; Attux, Romis ^{[3, 1]} ; Castellano, Gabriela ^{[1, 2]}

Afiliação do(s) autor(es)

^[1] Brazilian Inst Neurosci & Neurotechnol BRAINN, DRCC Rua Sergio Buarque Holanda 777, Campinas, SP - Brazil

^[2] Univ Estadual Campinas, Neurophys Grp, Gleb Wataghin Phys Inst, DRCC Rua Sergio Buarque Holanda 777, Campinas, SP - Brazil

^[3] Univ Estadual Campinas, Dept Comp Engn & Ind Automat, Sch Elect & Comp Engn, Campinas, SP - Brazil

• Tipo de documento: Artigo Científico
• Fonte: Biomedical Signal Processing and Control; v. 40, p. 359-365, FEB 2018.
• Citações Web of Science: 8

Resumo

The study of motor imagery (MI) has been a subject of great interest within the brain-computer interface (BCI) community. Several approaches have been proposed to solve the problem of classifying cerebral responses due to MI, mostly based on the power spectral density of the mu and beta bands; however, no optimum manner of proceeding through the fundamental steps of a MI-BCI has yet been established. In this work, we explored a relatively novel approach regarding feature generation for a MI-BCI by assuming that functional connectivity patterns of the brain are altered during hand MI. We modelled interactions among EEG electrodes by a graph, extracted metrics from it during left and right hand MI from eight subjects and classified the signals using commonly employed techniques in the BCI community (LDA and SVM). We also compared this approach to the more established method of using the signal power spectral density as the classifier features. With the graph method, we confirmed that only specific electrodes provide relevant information for data classification. A first approach provided maximum average classification rates across all subjects for the graph method of 86% for the mu band and 87% for the beta band. For the PSD method, average rates were of 98% and 99% for the mu and beta bands, respectively. However, a much larger number of features was needed: (130 44) and (273 89) for the mu and beta bands, respectively. Aiming to reproduce these rates using the graph method, pairwise inputs combinations of graph metrics were tested. They proved to be sufficient to obtain essentially the same classification accuracy rates, but with a considerably smaller number of features - about 60 features, for both bands. We thus conclude that the graph method is a feasible option for classification of hand MI signals. (C) 2017 Elsevier Ltd. All rights reserved. (AU)

• Processo FAPESP: 13/07559-3 - Instituto Brasileiro de Neurociência e Neurotecnologia - BRAINN
• Beneficiário: Fernando Cendes
• Modalidade de apoio: Auxílio à Pesquisa - Centros de Pesquisa, Inovação e Difusão - CEPIDs