Space-time recurrences for functional connectivity evaluation and feature extraction in motor imagery brain-computer interfaces

Autor(es)

Rodrigues, Paula G. ^{[1, 2]} ; Stefano Filho, Carlos A. ^{[1, 3]} ; Attux, Romis ^{[1, 4]} ; Castellano, Gabriela ^{[1, 3]} ; Soriano, Diogo C. ^{[1, 2]}

Afiliação do(s) autor(es)

^[1] Brazilian Inst Neurosci & Neurotechnol BRAINN, Campinas, SP - Brazil

^[2] Fed Univ ABC UFABC, Engn Modeling & Appl Social Sci Ctr CECS, Sao Bernardo Do Campo, SP - Brazil

^[3] Univ Estadual Campinas, UNICAMP, Neurophys Grp, Inst Phys Gleb Wataghin IFGW, Campinas, SP - Brazil

^[4] Univ Estadual Campinas, Sch Elect & Comp Engn FEEC, Campinas, SP - Brazil

• Tipo de documento: Artigo Científico
• Fonte: MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING; v. 57, n. 8, p. 1709-1725, AUG 2019.
• Citações Web of Science: 1

Resumo

This work presents a classification performance comparison between different frameworks for functional connectivity evaluation and complex network feature extraction aiming to distinguish motor imagery classes in electroencephalography (EEG)-based brain-computer interfaces (BCIs). The analysis was performed in two online datasets: (1) a classical benchmark-the BCI competition IV dataset 2a-allowing a comparison with a representative set of strategies previously employed in this BCI paradigm and (2) a statistically representative dataset for signal processing technique comparisons over 52 subjects. Besides exploring three classical similarity measures-Pearson correlation, Spearman correlation, and mean phase coherence-this work also proposes a recurrence-based alternative for estimating EEG brain functional connectivity, which takes into account the recurrence density between pairwise electrodes over a time window. These strategies were followed by graph feature evaluation considering clustering coefficient, degree, betweenness centrality, and eigenvector centrality. The features were selected by Fisher's discriminating ratio and classification was performed by a least squares classifier in agreement with classical and online BCI processing strategies. The results revealed that the recurrence-based approach for functional connectivity evaluation was significantly better than the other frameworks, which is probably associated with the use of higher order statistics underlying the electrode joint probability estimation and a higher capability of capturing nonlinear inter-relations. There were no significant differences in performance among the evaluated graph features, but the eigenvector centrality was the best feature regarding processing time. Finally, the best ranked graph-based attributes were found in classical EEG motor cortex positions for the subjects with best performances, relating functional organization and motor activity. (AU)

• Processo FAPESP: 17/10341-0 - Investigação da técnica de treinamento por neurofeedback utilizando ressonância magnética
• Beneficiário: Carlos Alberto Stefano Filho
• Modalidade de apoio: Bolsas no Brasil - Doutorado
• 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
• Processo FAPESP: 15/24260-7 - Extração de Características em Interfaces Cérebro-Máquina Utilizando Métricas de Redes Complexas
• Beneficiário: Paula Gabrielly Rodrigues
• Modalidade de apoio: Bolsas no Brasil - Mestrado