Application of convolutional neural networks for EEG signals classification in brain-computer interfaces

• Grant number: 19/17997-4
• Support Opportunities: Scholarships in Brazil - Scientific Initiation
• Start date: July 01, 2020
• End date: June 30, 2021
• Field of knowledge: Physical Sciences and Mathematics - Computer Science - Computer Systems
• Principal Investigator: Denis Gustavo Fantinato
• Grantee: Patrick Oliveira de Paula
• Host Institution: Centro de Matemática, Computação e Cognição (CMCC). Universidade Federal do ABC (UFABC). Ministério da Educação (Brasil). Santo André , SP, Brazil

Abstract

Brain-Computer Interfaces (BCI) has been subject of great attention for its potential applications in a myriad of contexts, for example, in assistive, rehabilitation and entertainment technologies. Significant advances, like collecting data with non-invasive methods by means of electroencephalograms (EEG), motivates the study and development of this promising interface. However, the broad variability of patterns observed in users of BCI, and its application in increasing sophisticated contexts, makes its use a very challenging problem. In this sense, this research project aims at applying Artificial Neural Networks for improvement of BCI systems, making them more efficient and robust. More specifically, we will focus on Convolutional Neural Networks, an Artificial Neural Network for Deep Learning with great potential for multidimensional data processing, like images and videos. In order to explore the full potential of this structure, a number of types of EEG patterns mappings will be used for building an input profile for the Neural Network.

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)

DE PAULA, PATRICK OLIVEIRA; DA SILVA COSTA, THIAGO BULHOES; DE FAISSOL ATTUX, ROMIS RIBEIRO; FANTINATO, DENIS GUSTAVO. Classification of image encoded SSVEP-based EEG signals using Convolutional Neural Networks. EXPERT SYSTEMS WITH APPLICATIONS, v. 214, p. 11-pg., 2023-03-15. (19/17997-4, 20/10014-2)