A question about the Euclidean mean
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In Table II of the paper, the input space dismension of MI1 is 6*200. The features are "six log-variance features of the CSP filtered trials". My question is how to compute the covariance matrix using these features? For each sample, we only have 6 such features and no time-series data, but according to the algorithm, we need to calculate the Euclidean mean with the features's covariance? Thank you in advance for your answer!
Hi, the Table II in our paper shows the the dimensionalities of different input spaces, the "input space" denotes different feature space. For your concern, the 6*200 means the CSP features in the Euclidean space, its standard baseline in Euclidean space is the CSP+LDA, so we don't need to calculate the Euclidean mean. Only for the tangent and Riemanian space based methods, we need to calculate the Riemanian/Tangent/Euclidean mean on the raw time-series data.
I see. Thank you for your reply!