Classification of image encoded SSVEP-based EEG signals using Convolutional Neural Networks

Autor(es)

de Paula, Patrick Oliveira ; da Silva Costa, Thiago Bulhoes ; de Faissol Attux, Romis Ribeiro ; Fantinato, Denis Gustavo

• 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