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Deep learning and brain-computer interfaces

Grant number: 18/04100-3
Support type:Scholarships in Brazil - Master
Effective date (Start): July 01, 2018
Effective date (End): August 31, 2019
Field of knowledge:Engineering - Electrical Engineering - Telecommunications
Cooperation agreement: Coordination of Improvement of Higher Education Personnel (CAPES)
Principal researcher:Romis Ribeiro de Faissol Attux
Grantee:Willian Rampazzo
Home Institution: Faculdade de Engenharia Elétrica e de Computação (FEEC). Universidade Estadual de Campinas (UNICAMP). Campinas , SP, Brazil
Associated research grant:13/07559-3 - BRAINN - The Brazilian Institute of Neuroscience and Neurotechnology, AP.CEPID


Neural networks are part of a well-established research field within the broad subject of machine learning. In the last years, this field has received a great deal of attention due to the growing interest in deep neural networks (DNNs). These networks have a structure characterized by the presence of several processing layers, which are responsible for a feature extraction process that, ideally, avoids the need for feature engineering. The approach has proven itself extremely relevant in fields as distinct as image recognition and automated text translation.In this project, we will propose DNNs that perform, using raw eletroencephalography data, the classification task inherent to a brain-computer interface (BCI). BCIs based on evoked potentials and motor imagery will be considered. The results will be compared to those obtained with classical BCIs, which treat the processes of feature extraction and classification in separate. (AU)

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Academic Publications
(References retrieved automatically from State of São Paulo Research Institutions)
RAMPAZZO, Willian. Deep learning and brain-computer interfaces. 2019. 99 f. Master's Dissertation - Universidade Estadual de Campinas (UNICAMP). Faculdade de Engenharia Elétrica e de Computação.

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