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

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.

Automated summary

Research on brain-computer interface (BCI) systems based on electroencephalography (EEG) signals is rapidly advancing, with a focus on achieving robust performance. Recently, deep learning methods, specifically convolutional neural networks (CNNs), have been applied to BCI systems to enhance performance. This study encodes EEG data as images and uses 2D-kernel-based CNNs for classification, yielding favorable results.