Method for finding low-variability EEG channels for motor imagery detection

• Grant number: 23/12422-9
• Support Opportunities: Scholarships in Brazil - Post-Doctoral
• Start date: January 01, 2024
• End date: December 31, 2025
• Field of knowledge: Physical Sciences and Mathematics - Physics
• Principal Investigator: Gabriela Castellano
• Grantee: Gabriel Chaves de Melo
• Host 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

A Brain-Computer Interface (BCI) measures brain activity and translates it into commands to a computer. The most widely adopted technique for brain signal recording towards BCI is electroencephalography (EEG). Despite the many advances towards practical-use BCIs in terms of hardware and computational techniques, this is still a goal to be achieved. The EEG signal variability might be the main reason for this to be. This means that the EEG signals concerning some task execution will be different across multiple executions of the same task, both intersubjects and intrasubject. Thus, it becomes extremely difficult to achieve sufficiently high accuracy while detecting these activities in real-time so it could be suitable for a BCI. This is due to the impossibility of measuring localized cortical activities with EEG. The main reasons for this are the poor spatial resolution inherent to the technique and the inexistence of an electrically neutral site to place the reference electrode. In the scientific literature, it is possible to find many strategies for dealing with such challenges. However, they end up including other kinds of uncertainties to the signals, which also cause variability. The present work proposes a method to find pairs of electrodes to act as EEG channels with low variability, without making use of mathematical or statistical techniques that would transform the signals introducing their own source of uncertainties. By doing so, we expect to map the cortical activation during a motor imagery (MI) task and use this information to detect the occurrence of the MI. If this phase is successful, the present project will make it possible to take new steps towards the practical use of BCIs for rehabilitation.

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 MELO, GABRIEL CHAVES; CASTELLANO, GABRIELA; FORNER-CORDERO, ARTURO. Identification and analysis of reference-independent movement event-related desynchronization. BIOMEDICAL PHYSICS & ENGINEERING EXPRESS, v. 11, n. 2, p. 18-pg., 2025-03-31. (23/12422-9, 13/07559-3)