The cerebral cortex of primates is endowed with neurons specifi- cally tuned for biological actions. These neurons are located in a network of areas comprising the visual areas of the region of the superior temporal sulcus (STS) and the visuomotor areas of the inferior parietal lobule and premotor cortex. It is generally assumed that action understanding depends on a serial recruitment of these areas. The observed actions, following an initial processing in striate and extrastriate visual areas, are encoded in STS. Subsequently, they are transformed into a motor format in the parietal and premotor areas. This transformation is done via the mirror mechanism. Here we present evidence for a fundamental role in action perception of backward projections to the occipital lobe. The evidence is based on two studies. In the first one, using high-density EEG, we showed that, during hand- action observation, following an early activation of occipital, parietal and premotor areas, late waves occur in the occipital lobe; in the second study, using TMS, we showed a clear impairment of action perception following occipital stimulation at the time of the late occipital waves. We conclude that, backward projections from motor cortex ‘bind’ the understanding of the goal of an action with the pictorial descriptions of the same action. This binding allows the full perception of the observed actions as a joint function of visual and motor areas and overcomes the traditional functional separation between the two systems

Automatic estimation of current dipoles from biomagnetic data is still a problematic task. This is due not only to the ill-posedness of the inverse problem but also to two intrinsic difficulties introduced by the dipolar model: the unknown number of sources and the nonlinear relationship between the source locations and the data. Recently, we have developed a new Bayesian approach, particle filtering, based on dynamical tracking of the dipole constellation. Contrary to many dipole-based methods, particle filtering does not assume stationarity of the source configuration: the number of dipoles and their positions are estimated and updated dynamically during the course of the MEG sequence. We have now developed a Matlab-based graphical user interface, which allows nonexpert users to do automatic dipole estimation from MEG data with particle filtering. In the present paper, we describe the main features of the software and show the analysis of both a synthetic data set and an experimental dataset. © 2011 C. Campi et al.

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We present a comparison of three methods for the solution of the magnetoencephalography inverse problem. The methods are: a linearly constrained minimum variance beamformer, an algorithm implementing multiple signal classification with recursively applied projection and a particle filter for Bayesian tracking. Synthetic data with neurophysiological significance are analyzed by the three methods to recover position, orientation and amplitude of the active sources. Finally, a real data set evoked by a simple auditory stimulus is considered.