title: Separable Common Spatio-Spectral Patterns for Motor Imagery BCI Systems.
abstract: OBJECTIVE
  Feature extraction is one of the most important steps in any brain-computer interface (BCI) system. In particular, spatio-spectral feature extraction for motor-imagery BCIs (MI-BCI) has been the focus of several works in the past decade. This paper proposes a novel method, called separable common spatio-spectral patterns (SCSSP), for extraction of discriminant spatio-spectral EEG features in MI-BCIs.
  
  
  METHODS
  Assuming a binary classification problem, SCSSP uses a heteroscedastic matrix-variate Gaussian model for the multiband EEG rhythms, and seeks the spatio-spectral features whose variance is maximized for one brain task and minimized for the other task. Therefore, SCSSP can be considered as a spatio-spectral generalization of the conventional common spatial patterns (CSP) algorithm.
  
  
  RESULTS
  The experimental results on two-class and multiclass motor-imagery data from publicly available BCI Competition datasets demonstrate that the proposed computationally efficient method competes closely with filter-bank CSP (FBCSP), and can even outperform the FBCSP if enough training data are available. Furthermore, SCSSP provides us with a simple measure for ranking the discriminant power of extracted spatio-spectral features, which is not possible in FBCSP.
  
  
  CONCLUSIONS
  The matrix-variate Gaussian assumption allows the SCSSP method to jointly process the EEG data in both spatial and spectral domains. As a result, compared to the similar solutions in the literature such as FBCSP, the proposed SCSSP method requires significantly lower computations.
  
  
  CONCLUSIONS
  The proposed computationally efficient spatio-spectral feature extractor is particularly suitable for applications in which the computational power is limited, such as emerging wearable mobile BCI systems.
authors.forename: Amirhossein S
authors.initials: AS
authors.lastname: Aghaei
authors.forename: Mohammad Shahin
authors.initials: MS
authors.lastname: Mahanta
authors.forename: Konstantinos N
authors.initials: KN
authors.lastname: Plataniotis
mesh_terms: D001921:Brain / Q000502:physiology*; D062207:Brain-Computer Interfaces; D004569:Electroencephalography / Q000145:classification* / Q000379:methods*; D006801:Humans; D007092:Imagination / Q000502:physiology*; D010363:Pattern Recognition, Automated; D012815:Signal Processing, Computer-Assisted