Abstract
A new processing framework that allows detailed characterization of the nonlinear dynamics of EEG signals at real-time rates is proposed. In this framework, the phase space trajectory is reconstructed and the underlying dynamics of the brain at different mental states are identified by analyzing the shape of this trajectory. Two sets of features based on affine-invariant moments and distance series transform allow robust estimation of the properties of the phase space trajectory while maintaining real-time performance. We describe the methodological details and practical implementation of the new framework and perform experimental verification using datasets from BCI competitions II and IV. The results showed excellent performance for using the new features as compared to competition winners and recent research on the same datasets providing best results in Graz2003 dataset and outperforming competition winner in 6 out of 9 subject in Graz2008 dataset. Furthermore, the computation times needed with the new methods were confirmed to permit real-time processing. The combination of more detailed description of the nonlinear dynamics of EEG and meeting online processing goals by the new methods offers great potential for several time-critical BCI applications such as prosthetic arm control or mental state monitoring for safety. (C) 2017 Elsevier Ltd. All rights reserved.