Um estudo sobre o processamento digital de sinais cerebrais no contexto da automação industrial aplicada à saúde

Abstract

This article presents a theoretical-conceptual review on the digital processing of brain signals, with emphasis on the integration of neuroscience, industrial automation, and data science. It discusses the neurobiological foundations of neuronal electrical activity, including action potentials and synapses, as well as their alterations in mental disorders such as anxiety, depression, and schizophrenia. The article then analyzes the functioning and applications of neural signal acquisition techniques, such as electroencephalography (EEG), magnetoencephalography (MEG), electrocorticography (ECoG), stereoelectroencephalography (sEEG), and brain-computer interfaces (BCIs), highlighting the brain-computer interfaces (BCIs), highlighting the technical and computational aspects involved. The research highlights the role of biomedical automation as an emerging field for industrial automation technologists, especially in the development of embedded systems and the integration between hardware and software for clinical applications. Advanced signal processing approaches are also explored, including machine learning and deep neural networks, which enable the extraction of useful patterns for motor rehabilitation, diagnostics, and assistive control. It is concluded that the convergence between engineering and neuroscience represents a promising field for the development of innovative health technologies, aligned with the applied technical training provided by the Industrial Automation program.