A Kronecker Product Structured EEG Covariance Estimator for a Language Model Assisted-BCI

  • Paula Gonzalez-NavarroEmail author
  • Mohammad Moghadamfalahi
  • Murat Akcakaya
  • Deniz Erdogmus
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9743)


Electroencephalography (EEG) recorded from multiple channels is typically used in many non-invasive brain computer interfaces (BCIs) for inference. Usually, EEG is assumed to be a Gaussian process with unknown mean and covariance, and the estimation of these parameters are required for BCI inference. However, relatively high dimensionality of the feature vectors extracted from the recorded EEG with respect to the number of supervised observations usually leads to a rank deficient covariance matrix estimator. In our typing BCI, RSVP Keyboard™, we solve this problem by applying regularization on the maximum likelihood covariance matrix estimators. Alternatively, in this manuscript we propose a Kronecker product structure for covariance matrices. Our underlying hypothesis is that the a structure imposed on the covariance matrices will improve the estimation accuracy and accordingly will result in typing performance improvements. Through an offline analysis we assess the classification accuracy of the proposed model. The results represent a significant improvement in classification accuracy compared to an RDA approach which does not assume any structure on the covariance.


Structured covariances kronecker Brain-Computer Interface (BCI) Spatial temporal discriminant analysis Event-Related Potential (ERP) Multichannel Electroencephalogram (EEG) 



This work is supported by NIH 2R01DC009834, NIDRR H133E140026, NSF CNS-1136027, IIS-1118061, IIS-1149570, CNS-1544895, SMA-0835976. For supplemental materials, please visit for the CSL Collection in the Northeastern University Digital Repository System.


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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Paula Gonzalez-Navarro
    • 1
    Email author
  • Mohammad Moghadamfalahi
    • 1
  • Murat Akcakaya
    • 2
  • Deniz Erdogmus
    • 1
  1. 1.Northeastern UniversityBostonUSA
  2. 2.University of PittsburghPittsburghUSA

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