Abstract — In electromagnetic source analysis it is necessary to determine how many sources are required to describe the EEG or MEG adequately. Model selection procedures (MSP’s, or goodness of fit procedures) give an estimate of the required number of sources. Existing and new MSP’s are evaluated in different source and noise settings: two sources which are close or distant, and noise which is uncorrelated or correlated. The commonly used MSP residual variance is seen to be ineffective, that is it often selects too many sources. Alternatives like the adjusted Hotelling’s test, Bayes information criterion, and the Wald test on source amplitudes are seen to be effective. The adjusted Hotelling’s test is recommended if a conservative approach is taken, and MSP’s such as Bayes information criterion or the Wald test on source amplitudes are recommended if a more liberal approach is desirable. The MSP’s are applied to empirical data (visual evoked fields). I.

In radio telescope arrays, the complex receiver gains and sensor noise powers are initially unknown and have to be calibrated. Gain calibration can enhance the quality of astronomical sky images and, moreover, improve the effectiveness of array signal processing techniques for interference mitigation and spatial filtering. A challenging aspect is that the signal-to-noise ratio (SNR) is usually well below 0 dB, even for the brightest sky sources. The calibration method considered here consists of observing a single point source and extracting the gain and noise parameters from the estimated covariance matrix. We present several closed-form and iterative identification algorithms for this. Weighted versions of the algorithms are proven to be asymptotically efficient. The algorithms are validated by simulations and application to experimental data observed at the Westerbork Synthesis Radio Telescope (WSRT).