1 code implementation • 5 Sep 2021 • Pedro R. A. S. Bassi, Romis Attux
Objective: To propose novel SSVEP classification methodologies using deep neural networks (DNNs) and improve performances in single-channel and user-independent brain-computer interfaces (BCIs) with small data lengths.
1 code implementation • 12 Apr 2021 • Pedro R. A. S. Bassi, Romis Attux
Purpose: we evaluated the generalization capability of deep neural networks (DNNs), trained to classify chest X-rays as Covid-19, normal or pneumonia, using a relatively small and mixed dataset.
no code implementations • 8 Oct 2020 • Pedro R. A. S. Bassi, Willian Rampazzo, Romis Attux
The presented methodology surpassed performances obtained with FBCCA and SVMs (more traditional SSVEP classification methods) in BCIs with small data lengths and one electrode.
no code implementations • 28 Jun 2020 • José Augusto Stuchi, Levy Boccato, Romis Attux
Machine learning applied to computer vision and signal processing is achieving results comparable to the human brain on specific tasks due to the great improvements brought by the deep neural networks (DNN).
1 code implementation • 11 Jun 2020 • Guilherme D. Pelegrina, Renan D. B. Brotto, Leonardo T. Duarte, Romis Attux, João M. T. Romano
In dimensionality reduction problems, the adopted technique may produce disparities between the representation errors of different groups.
2 code implementations • 30 Apr 2020 • Pedro R. A. S. Bassi, Romis Attux
Purpose: We present image classifiers based on Dense Convolutional Networks and transfer learning to classify chest X-ray images according to three labels: COVID-19, pneumonia and normal.