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

Author(s)

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

• Document type: Journal article
• Source: EXPERT SYSTEMS WITH APPLICATIONS; v. 214, p. 11-pg., 2023-03-15.

Abstract

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)

• FAPESP's process: 19/17997-4 - Application of convolutional neural networks for EEG signals classification in brain-computer interfaces
• Grantee: Patrick Oliveira de Paula
• Support Opportunities: Scholarships in Brazil - Scientific Initiation
• FAPESP's process: 20/10014-2 - Deep learning techniques for brain-computer interface systems
• Grantee: Denis Gustavo Fantinato
• Support Opportunities: Regular Research Grants