Investigation of the stability of functional brain networks obtained from electroencephalography data for application in brain-computer interfaces

• Grant number: 21/06397-6
• Support type: Scholarships in Brazil - Master
• Effective date (Start): August 01, 2021
• Effective date (End): July 31, 2023
• Field of knowledge: Engineering - Biomedical Engineering
• Principal researcher: Gabriela Castellano
• Grantee: Pedro Felipe Giarusso de Vazquez
• Home Institution: Instituto de Física Gleb Wataghin (IFGW). Universidade Estadual de Campinas (UNICAMP). Campinas , SP, Brazil
• Associated research grant: 13/07559-3 - BRAINN - The Brazilian Institute of Neuroscience and Neurotechnology, AP.CEPID

Abstract

Brain-computer interfaces (BCIs) are systems that translate brain signals directly into commands for an external device, without going through the usual neural pathways. Although in principle, any technique for measuring brain dynamics can be used in a BCI, electroencephalography (EEG) has been the most used, mainly due to its high temporal resolution, portability and relatively low cost. Motor imagery-based EEG-BCIs (MI) have been used as auxiliary tools for motor rehabilitation of different types of patients. However, EEG-BCIs still lack robustness in order to be adopted in clinical routine. This is due to the high intra- and inter-individual variability of the EEG signals and the noise and artifacts present in these signals, which result in great difficulty in obtaining reproducible characteristics of these signals that can be used in BCIs. The most used features of EEG signals for MI-based BCIs are the signal power in specific frequency bands (mu, from ~8 to 12 Hz, and beta, from ~12 to 30 Hz). For the past years, our group has been investigating alternative features, obtained from brain functional networks, through graph theory, for use in this context. The aim of this project is to investigate the stability of features obtained from functional brain networks over several acquisitions of various individuals, to assess which would be the most suitable feature to be used in the context of MI-based EEG-BCIs. For this, we will use a database collected in the PhD project of a student of the group (Carlos Alberto Stefano Filho, FAPESP Process 2016/22116-9), which consists of 10 EEG acquisitions for 20 individuals, with a 64-channel equipment. In this way, the project will not require a new data collection, and can be carried out entirely online, should the Covid-19 pandemic last longer. This study will be important for the research of the Neurophysics Group, under CEPID BRAINN, related to the development of assistive and rehabilitation technologies for neurological patients. (AU)