| Texto completo | |
| Autor(es): |
de Paula, Patrick Oliveira
;
da Silva Costa, Thiago Bulhoes
;
de Faissol Attux, Romis Ribeiro
;
Fantinato, Denis Gustavo
Número total de Autores: 4
|
| Tipo de documento: | Artigo Científico |
| Fonte: | EXPERT SYSTEMS WITH APPLICATIONS; v. 214, p. 11-pg., 2023-03-15. |
| Resumo | |
Brain-Computer Interfaces (BCI) systems based on electroencephalography (EEG) signals are experiencing a rapid development, counting with a number of methods, mainly from signal processing and machine learning areas. Although important results have been achieved, a robust performance is still a very challenging task, mainly considering high intra- and inter-subject variability in EEG data and long acquisition time intervals. Recently, Deep Learning methods, such as the Convolutional Neural Networks (CNNs), are being used in BCI systems in search of a performance improvement. However, the straightforward use of EEG data, without any processing step, may limit the full potential of 2D-kernels in CNNs. In light of this, in this work, we consider for classification with 2D-kernel-based CNNs the problem of encoding EEG data to images as a pre-processing stage, which includes the Gramian Angular Difference and Summation Fields, Markov Transition Fields and Recurrence Plots. Additionally, a comparative analysis using a selection of CNNs is performed. Results show a favorable performance for the proposed method, pointing towards a robust BCI system using cross-subject data, with short acquisition time interval. (AU) | |
| Processo FAPESP: | 19/17997-4 - Uso de Redes Neurais Convolucionais para Classificação de Sinais de EEG em Sistemas de Interface Cérebro-Computador |
| Beneficiário: | Patrick Oliveira de Paula |
| Modalidade de apoio: | Bolsas no Brasil - Iniciação Científica |
| Processo FAPESP: | 20/10014-2 - Técnicas de aprendizado profundo no desenvolvimento de sistemas de interface cérebro-computador |
| Beneficiário: | Denis Gustavo Fantinato |
| Modalidade de apoio: | Auxílio à Pesquisa - Regular |