Feature Extraction in Brain-Computer Interfaces Using Complex Networks Metrics

• Grant number: 15/24260-7
• Support type: Scholarships in Brazil - Master
• Effective date (Start): March 01, 2016
• Effective date (End): February 28, 2018
• Field of knowledge: Engineering - Biomedical Engineering
• Principal researcher: Diogo Coutinho Soriano
• Grantee: Paula Gabrielly Rodrigues
• Home Institution: Centro de Engenharia, Modelagem e Ciências Sociais Aplicadas (CECS). Universidade Federal do ABC (UFABC). Ministério da Educação (Brasil). Santo André , SP, Brazil

Abstract

The theory of complex networks has been successful in the attempt to provide information and a suitably representation of the relation between elements of distinct nature in different contexts. Recently, means of transforming time series in graphs have been attracting attention of researchers aiming to characterize physical process, specially, the biological or physiological ones, by means of its classical measures (degree, mean path length, cluster coefficient, etc). In this context, this master degree thesis aims to study a methodology for converting time series into complex networks for pattern recognition of EEG signals collected into the framework of brain-computer interface systems.

Scientific publications

(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)

RODRIGUES, PAULA G.; STEFANO FILHO, CARLOS A.; ATTUX, ROMIS; CASTELLANO, GABRIELA; SORIANO, DIOGO C. Space-time recurrences for functional connectivity evaluation and feature extraction in motor imagery brain-computer interfaces. MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING, v. 57, n. 8, p. 1709-1725, AUG 2019. Web of Science Citations: 1.