A kronecker product structured EEG covariance estimator for a language model assisted-BCI (Gonzalez-Navarro & Moghadamfalahi, 2016)

Abstract: 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 KeyboardTM, 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.
Submitted to NARIC database