Characterization of phase space trajectories for Brain-Computer Interface. (September 2017)

• Record Type: Journal Article
• Authors: Sayed, Khaled; Kamel, Mahmoud; Alhaddad, Mohammed; Malibary, Hussein M.; Kadah, Yasser M.
• Is Part Of: Biomedical signal processing and control. Volume 38(2017)
• Page Start: 55
• Page End: 66
• Publication Date: 2017-09
• Subjects: Brain-Computer Interface (BCI) -- Electroencephalogram (EEG) -- Distance series (DS) -- Moment invariants -- Phase space reconstruction (PSR); Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2017.05.007
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: New features from phase space trajectories of EEG nonlinear dynamics are proposed. Moment invariant features allow robust description of phase space trajectory. Distance series transforms trajectory to 1D signal to be used as features. Experimental results indicate potential for new features in BCI applications. Proposed features allow real-time performance needed for BCI applications. 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.