Classification Performance of SSVEP Brain-Computer Interfaces Based on Functional Connectivity

Author(s)

Rodrigues, Paula G. ; Silva Junior, Jose I. ; Costa, Thiago B. S. ; Attux, Romis ; Castellano, Gabriela ; Soriano, Diogo C. ; CostaFelix, R ; Machado, JC ; Alvarenga, AV

• Document type: Journal article
• Source: XXVI BRAZILIAN CONGRESS ON BIOMEDICAL ENGINEERING, CBEB 2018, VOL. 2; v. 70, n. 2, p. 6-pg., 2019-01-01.

Abstract

Brain connectivity analysis via complex networks has been widely applied to elucidate functional aspects related to brain diseases, such as Alzheimer and Parkinson, and, more recently, to investigations concerning the functional organization of brain regions under motor imagery in brain computer interfaces (BCIs). Therefore, this work seeks to investigate the classification performance of steady-state visually evoked potential (SSVEP) brain-computer interfaces based on functional connectivity. Two different approaches were chosen for extracting functional connectivity and estimating the adjacency matrix from SSVEP-EEG signals: classical Pearson correlation and a new proposal based on Space-Time recurrence counting. These strategies were followed by graph feature evaluation (clustering coefficient, degree, betweenness and eigenvalue centralities), feature selection via Davies-Bouldin index and classification using a least squares classifier for 15 subjects in a 4-command SSVEP-BCI system. For comparison, we also employed a classical spectral feature extraction approach based on the fast Fourier transform (FFT). It was observed that it is possible to separate the classes with a mean accuracy of 0.56 for Pearson and 0.61 for the STR framework, with the clustering coefficient and the eigenvector centrality being the best attributes for these scenarios, respectively. Nonetheless, classical FFT-based feature extraction obtained the best decoding performance. (AU)

• 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
• 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