Improving the Event-related Potential Classi cation Performance of the Riemannian Pipeline using Shrinkage Regularization

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The aim in the eld of brain-computer interfaces is to control an electronic device using brain responses that are recorded in an electroencephalogram. These brain signals can be decoded by means of the Riemannian metrics that are associated with the Riemannian manifold. This way of classifying allows for transfer learning possibilities and is more robust opposite to the Euclidean approach. However, to get to these desired characteristics, the data points that represent a single brain response need to be represented by their covariance matrices. This is a drawback of the framework, as the sample covariance matrix is a sub-optimal estimator of the true covariance matrix when the number of samples is low compared to the number of features. To improve the estimator and enhance the classi cation performance, I apply shrinkage regularization on the di erent submatrices of the epoch-based covariance matrix. To assess the e ect of this method, I compare two decoding algorithms against a baseline using six datasets. The results show that there is a signi cant increase in classi cation performance for one out of the six datasets.
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