Optimal design for EEG/MEG source analysis
نویسنده
چکیده
The regression model is y = f(X,θ) + ε . The vector y contains potentials measured on N sensors. f(X,θ) is the vector with modeled potentials. X is the matrix with sensor coordinates, θ the vector with P source parameters of D sources, and ε the noise vector. Let G be the N by P matrix with the first order partial derivatives of the N modeled potentials to the P parameters. The covariance matrix of the parameter estimates is then [2]:
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