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

• Published in: Expert systems with applications Vol. 214; p. 119096
• Main Authors: de Paula, Patrick Oliveira, da Silva Costa, Thiago Bulhões, de Faissol Attux, Romis Ribeiro, Fantinato, Denis Gustavo
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 15.03.2023
• Subjects: Brain–Computer Interfaces; Convolutional Neural Networks; Deep Learning; Brain–Computer Interfaces; Deep Learning; Convolutional Neural Networks
• ISSN: 0957-4174
• DOI: 10.1016/j.eswa.2022.119096

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. •Use of image encoding methods as a preprocessing tool for EEG/SSVEP signals.•Use of Convolutional Neural Networks for classification of image encoded EEG signals.•Comparative performance analysis with multiple imaging methods and neural networks.•The imaging methods allows robust classification performance for cross subject data.