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The dependence of the neuromagnetic source localization accuracy on the volume conductor model was studied by the analysis of measured magnetic fields generated by tangentially oriented dipoles in a realistically shaped skull phantom. When using a homogeneous sphere model in the localization procedure, the errors were found to increase from about 3 mm to about 9 mm when the distance between the dipoles and the inner surface of the skull increased from 1 cm to 3 cm, whereas when using a true, realistic model in the inverse procedure the localization errors were only about 2-3 mm, independent of dipole depth. To account for the realistic geometry of the inner surface of the skull, the Boundary Element Method, based on a surface discretization in terms of about 300 triangles, proved to be sufficient. In addition to these analyses of experimental data, simulations were carried out to study the localization errors in the case of the spherical approximation for a dipole orientation changing from tangential to radial. For the latter orientation, errors of up to a few centimeters were found.

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Visuomotor tasks elicit neuronal activity in primate motor areas at relatively short latencies. Although this early activity embodies features of visual responses (short latency, stimulusdependency), its sensory nature has been questioned. We investigated neural correlates of visuomotor performance in human motor areas using scalp and intracranial event-related potential measures. A simple visuomanual reaction-time task evoked early potentials at 133±145 ms post-stimulus which occurred much earlier than the motor potentials of the same region. The amplitude of the early potentials covaried with stimulus location and was independent of parameters of the motor response. Because of their timing, stimulus-dependency and characteristics of our behavioral task, the early potentials are suggested to re¯ect neuronal responses of sensory nature rather than processing related to pure motor aspects of the task.

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A tomography of neural sources could be constructed from EEG/MEG recordings once the neuroelectromagnetic inverse problem (NIP) is solved. Unfortunately the NIP lacks a unique solution and therefore additional constraints are needed to achieve uniqueness. Researchers are then confronted with the dilemma of choosing one solution on the basis of the advantages publicized by their authors. This study aims to help researchers to better guide their choices by clarifying what is hidden behind inverse solutions oversold by their apparently optimal properties to localize single sources. Here, we introduce an inverse solution (ANA) attaining perfect localization of single sources to illustrate how spurious sources emerge and destroy the reconstruction of simultaneously active sources. Although ANA is probably the simplest and robust alternative for data generated by a single dominant source plus noise, the main contribution of this manuscript is to show that zero localization error of single sources is a trivial and largely uninformative property unable to predict the performance of an inverse solution in presence of simultaneously active sources. We recommend as the most logical strategy for solving the NIP the incorporation of sound additional a priori information about neural generators that supplements the information contained in the data.