Graduated in Electrical Engineering (2018) from the Faculty of Electrical and Computer Engineering (FEEC) of the State University of Campinas (UNICAMP). He holds a Master's Degree in Electrical Engineering from the same University (2020), in the Department of Biomedical Engineering (DEEB). His research theme in the master's degree was according to the following themes: Computational Neuroscience, Parkinson's Disease and Stochastic Resonance. He also had experience in the area of Signal Processing, in the topic of Blind Source Separation and Independent Components Analysis, and in the field of Internet of Things and Intelligent Systems. He is currently a PhD student at DEEB/FEEC/UNICAMP and his current research is focused on the study of artificial intelligence and machine learning algorithms for the decomposition of the high-density electromyographic signal (HD-EMG). (Source: Lattes Curriculum)
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Decomposition of surface High-Density Electromyographic signals (HD EMG) into motor unitspike trains opens a non-invasive window to neural mechanisms underlying movement control. The signals produced by EMG decomposition have a scientific (to understand the physiology of motor system) and technological (to the control of human-machine interfaces, such as myoelectric prosthesis) relevance.…
Parkinson's disease (PD) has been postulated to result from degeneration of dopaminergic neurons in the basal ganglia. The main symptoms of this neurodegenerative disease are: tremor, difficulty in the maintenance of posture, and uncoordinated movements. Parkinsonian tremor is thought to emerge from increased cortical oscillations, especially in alpha (8-15Hz) and beta (15-35Hz) bands. Th…
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