Deep learning and signal processing techniques applied to EEG data

• Grant number: 23/00640-1
• Support Opportunities: Regular Research Grants
• Start date: June 01, 2023
• End date: May 31, 2025
• Field of knowledge: Engineering - Electrical Engineering
• Principal Investigator: Denis Gustavo Fantinato
• Grantee: Denis Gustavo Fantinato
• Host Institution: Faculdade de Engenharia Elétrica e de Computação (FEEC). Universidade Estadual de Campinas (UNICAMP). Campinas , SP, Brazil
• Associated researchers: Leonardo Tomazeli Duarte ; Levy Boccato ; Rafael Ferrari ; Romis Ribeiro de Faissol Attux ; Sarah Negreiros de Carvalho Leite

Abstract

Electroencephalographic (EEG) data are records of brain electrical activity, with potential application in a wide range of contexts, ranging from medical diagnosis, assistive technologies and rehabilitation to entertainment devices. In that sense, with the consolidation of signal processing and machine learning techniques, the direct information transmission system between the brain and the computer, called the Brain-Computer Interface (BCI), can be highlighted. However, the high variability of patterns observed in EEG data from BCI users and the application of BCI in increasingly sophisticated contexts, make its use a very challenging problem. In this sense, this research project aims at applying Deep Learning and Signal Processing techniques for improvement of BCI systems, making them more efficient and robust. In the first stage, the focus shall be on the treatment of EEG signals through Independent Component Analysis (ICA) and Riemannian geometry, allowing an efficient feature extraction. In a second stage, deep learning networks focused on EEG data processing shall be used for classification. Their great potential to deal with high variability data shall be very useful for EEG-based BCI systems. (AU)