The Multiple Signal Classification (MUSIC) algorithm for acoustic imaging most commonly assumes that all sources have undirectional radiation pattern. We here propose a modification of this algorithm such that the concept is applicable for arbitrary directional characteristics of the sources. This is accomplished by fitting for each frequency the real valued amplitudes of the acoustic model rather than assuming a fixed functional form. The mathematical problem can be solved analytically resulting in an eigenvalue problem of a real valued Hamiltonian matrix. The performance is illustrated in simulations using pure monopolar, dipolar and quadrupolar sources.

We introduce a novel framework for estimating vector fields using sparse basis field expansions (S-FLEX). The notion of basis fields, which are an extension of scalar basis functions, arises naturally in our framework from a rotational invariance requirement. We consider a regression setting as well as inverse problems. All variants discussed lead to second-order cone programming formulations. While our framework is generally applicable to any type of vector field, we focus in this paper on applying it to solving the EEG/MEG inverse problem. It is shown that significantly more precise and neurophysiologically more plausible location and shape estimates of cerebral current sources from EEG/MEG measurements become possible with our method when comparing to the state-of-the-art.

When trying to understand the brain, it is of fundamental importance to analyse (e.g. from EEG/MEG measurements) what parts of the cortex interact with each other in order to infer more accurate models of brain activity. Common techniques like Blind Source Separation (BSS) can estimate brain sources and single out artifacts by using the underlying assumption of source signal independence. However, physiologically interesting brain sources typically interact, so BSS will--by construction-- fail to characterize them properly. Noting that there are truly interacting sources and signals that only seemingly interact due to effects of volume conduction, this work aims to contribute by distinguishing these effects. For this a new BSS technique is proposed that uses anti-symmetrized cross-correlation matrices and subsequent diagonalization. The resulting decomposition consists of the truly interacting brain sources and suppresses any spurious interaction stemming from volume conduction. Our new concept of interacting source analysis (ISA) is successfully demonstrated on MEG data.

The power change of electroencephalographs (EEG) rhythms is an indicator to investigate neuronal activation underlying EEG electrodes. We developed a software routine for analysis and visualization of spatiotemporal patterns of EEG oscillations: event-related desynchronization (ERD) and event-related synchronization (ERS). Further, the spatiotemporal representation of task-related coherence (TRC) was also proposed for investigation of interregional neuronal connectivity. As concurrent artifacts and volume conduction effect may result incorrect or spatially blurred estimation of EEG features, we have embedded data preprocessing functions to reduce these effects including: correcting eye movement-related artifact, changing reference methods and spatial filtering undesirable neuronal activity. These functions were subsequently applied to study EEG time-frequency patterns during self-paced movements in normal volunteers.