Development of algorithms and computational techniques for application in brain-computer interfaces

• Grant number: 16/02555-8
• Support Opportunities: Regular Research Grants
• Start date: August 01, 2016
• End date: July 31, 2018
• Field of knowledge: Physical Sciences and Mathematics - Computer Science
• Principal Investigator: João Luís Garcia Rosa
• Grantee: João Luís Garcia Rosa
• Host Institution: Instituto de Ciências Matemáticas e de Computação (ICMC). Universidade de São Paulo (USP). São Carlos , SP, Brazil
• Associated researchers: Norberto Garcia Cairasco

Abstract

Brain-Computer Interfaces (BCI) is a form of communication that enables individuals unable to perform movements to connect to external assistive devices using the electroencephalography (EEG) or other brain signals. Noninvasive BCIs capture changes in blood flow or fluctuations in electric and magnetic fields caused by the activity of large populations of neurons. The EEG, a non-invasive technique, measures the electrical activity of the brain in different locations of the head, typically using electrodes placed on the scalp. With the proper removal of artifacts, signal processing and machine learning, human EEG carries enough information about the intention of planning and execution. Brain models based on neurodynamics seek to understand and represent the reasons why neurons are excitable cells. The microscopic electric current of each neuron adds with the currents from other neurons, which causes a difference in macroscopic electric potential, measured by EEG, which records the patterns of populations of neurons mesoscopic activity. That is, a good neural model must reproduce the dynamics of neurons, taking into account the dynamic properties of populations of neurons, in addition to the electrophysiological properties of individual neurons. The objectives of the proposed tutorial are to show how the understanding of electrical activity of the brain, measured noninvasively by EEG, can provide a way to allow communication without muscle movements. The intention is, from the study of the neurodynamic behavior of the brain, to investigate ways and propose models that enable the noninvasive brain-computer interfaces. In recent decades, the EEG-based BCIs have attracted the attention of researchers in the fields of neuroscience, neural engineering and clinical rehabilitation. The plan is to use the data obtained through BCI to analyze the pre-motor movements, changes in the brain that occur before there is actually a movement, and apply them to a proper handling of prosthetic devices. (AU)

Scientific publications

(10)

(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)

BUSTIOS, PAUL; ROSA, JOAO LUIS; IEEE. Restricted Exhaustive Search for Frequency Band Selection in Motor Imagery Classification. 2017 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), v. N/A, p. 6-pg., 2017-01-01. (16/02555-8)

TALES OLIVA, JEFFERSON; GARCIA ROSA, JOAO LUIS; IEEE. Differentiation between Normal and Interictal EEG Using Multitaper Spectral Classifiers. 2018 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), v. N/A, p. 8-pg., 2018-01-01. (16/02555-8)

OLIVA, JEFFERSON TALES; GARCIA ROSA, JOAO LUIS. Classification for EEG report generation and epilepsy detection. Neurocomputing, v. 335, p. 81-95, MAR 28 2019. (16/02555-8)

CARVALHO, ISABELLE; GARCIA ROSA, JOAO LUIS; DOS SANTOS, KETLIN FABRI; ALVES, DOMINGOS; QUINTELAVARAJAO, JE; CRUZCUNHA, MM; MARTINHO, R; RIJO, R; DOMINGOS, D; PERES, E. Rule induction algorithms for classification of psychotic disorders involving social vulnerability features. PROCEEDINGS OF THE XI LATIN AND AMERICAN ALGORITHMS, GRAPHS AND OPTIMIZATION SYMPOSIUM, v. 138, p. 7-pg., 2018-01-01. (16/02555-8)

DE AGUIAR NETO, FERNANDO SOARES; GARCIA ROSA, JOAO LUIS. Depression biomarkers using non-invasive EEG: A review. NEUROSCIENCE AND BIOBEHAVIORAL REVIEWS, v. 105, p. 83-93, OCT 2019. (16/02555-8)

OLIVA, JEFFERSON TALES; GARCIA ROSA, JOAO LUIS. How an epileptic EEG segment, used as reference, can influence a cross-correlation classifier?. APPLIED INTELLIGENCE, v. 47, n. 1, p. 178-196, JUL 2017. (16/02555-8)

OLIVA, JEFFERSON TALES; GARCIA ROSA, JOAO LUIS; IEEE. Epilepsy detection using multiclass classifier based on spectral features. 2019 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), v. N/A, p. 8-pg., 2019-01-01. (16/02555-8)

OLIVA, JEFFERSON TALES; GARCIA ROSA, JOAO LUIS. Binary and multiclass classifiers based on multitaper spectral features for epilepsy detection. Biomedical Signal Processing and Control, v. 66, APR 2021. (16/02555-8)

CESTARI, DANIEL MOREIRA; ROSA, JOAO LUIS G.; IEEE. Stochastic and Deterministic Stationarity Analysis of EEG Data. 2017 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), v. N/A, p. 8-pg., 2017-01-01. (16/02555-8)

OLIVA, JEFFERSON TALES; GARCIA ROSA, JOAO LUIS; IEEE. The Use of One-Class Classifiers for Differentiating Healthy from Epileptic EEG Segments. 2017 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), v. N/A, p. 8-pg., 2017-01-01. (16/02555-8)