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

Author(s)

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

Affiliation

^[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

• Document type: Journal article
• Source: MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING; v. 57, n. 8, p. 1709-1725, AUG 2019.
• Web of Science Citations: 1

Abstract

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)

• FAPESP's process: 17/10341-0 - Investigation of the neurofeedback technique using MRI
• Grantee: Carlos Alberto Stefano Filho
• Support Opportunities: Scholarships in Brazil - Doctorate
• FAPESP's process: 13/07559-3 - BRAINN - The Brazilian Institute of Neuroscience and Neurotechnology
• Grantee: Fernando Cendes
• Support Opportunities: Research Grants - Research, Innovation and Dissemination Centers - RIDC
• FAPESP's process: 15/24260-7 - Feature Extraction in Brain-Computer Interfaces Using Complex Networks Metrics
• Grantee: Paula Gabrielly Rodrigues
• Support Opportunities: Scholarships in Brazil - Master