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Evaluation of the neural substrate of learning control of Brain-Computer Interface in healthy subjects

Grant number: 13/10952-9
Support type:Scholarships in Brazil - Master
Effective date (Start): January 01, 2014
Effective date (End): September 30, 2014
Field of knowledge:Interdisciplinary Subjects
Principal Investigator:João Ricardo Sato
Grantee:Lucas Remoaldo Trambaiolli
Home Institution: Centro de Matemática, Computação e Cognição (CMCC). Universidade Federal do ABC (UFABC). Ministério da Educação (Brasil). Santo André , SP, Brazil


The research line of brain-computer interfaces (BCI) seeks to use electrical brain signals recorded by electroencephalography (EEG), with the aim of providing interaction and control of electromechanical equipment, without the need of a user's motor behavior. One of the concepts used in studies of BCI utilizes neural signals stemming from motor imagery (MI) to control computers and/or robotic devices. The unilateral MI may result in lateralized activation of sensorimotor brain areas, similar to the one found in the preparatory phase of the movement of hands and feet.Although the literature does not present a lot of work with longitudinal assessment of the effects of MI, some studies found that the MI and mental practice of a movement triggers a reorganization of neural substrates, similar to achievement and physical practice of the same. Thus, this study aims to research longitudinal reorganization of synaptic connectivity arising from the use of BCI by healthy individuals as well as the variation in performance over time. To this end, the subjects held four sessions of the called paradigm of basketball, which should take a cursor moving to one of the targets at the bottom of the screen using only the hand MI.Besides the expected performance improvement will also be evaluated possible changes in neural connectivity measures, such as coherence and spectral power variation in the regions related to MI due to the learning of individuals.

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)
TRAMBAIOLLI, L. R.; SPOLAOR, N.; LORENA, A. C.; ANGHINAH, R.; SATO, J. R. Feature selection before EEG classification supports the diagnosis of Alzheimer's disease. CLINICAL NEUROPHYSIOLOGY, v. 128, n. 10, p. 2058-2067, OCT 2017. Web of Science Citations: 10.

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