Cortical Source Localization in EEG Frequency Analysis

In recent years some progresses have been made in acoustic source localization (ASL) in highly anisotropic plates when the plate material properties are not known. Some simplifying assumptions in many of the earlier source localization techniques that the wave propagates along a straight line from the source to the receiving sensor is not correct for many anisotropic plates. Wave energy in anisotropic solids generally propagate along curved lines and forms non-circular wave fronts. As a result, for highly anisotropic solids the acoustic source localization techniques that assume straight-line propagation of waves from the source to the receiver are bound to give erroneous results. In this paper earlier techniques as well as new developments for acoustic source localization in homogeneous isotropic and anisotropic plates are discussed. The newest source localization techniques in anisotropic plates analyze the non-circular shapes of the wave front. Following this analysis, the acoustic source has been successfully localized in some anisotropic plates without knowing the plate properties. Limitations of the current techniques that still exist and require further investigation are discussed.

Evaluation of different cortical source localization methods using simulated and experimental EEG data

The source localization problem concerns the detection and localization of an emitter whose transmission is observed by geographically separated receivers. The passive multistatic radar (PMR) problem concerns the detection and localization of a target that scatters an illumination signal to geographically separated receivers. By modeling the scattering target as an emitter, the techniques of source localization can be applied to the PMR problem. Indeed, this approach has recently been introduced in the literature. However, the exact relationship between the two problems has not been made explicit. In this work, we derive a centralized generalized likelihood ratio test (GLRT) detector that performs the processing characteristic of both source localization and PMR. This detector is used to assess the relative detection benefit provided by source localization in PMR. We show that source localization techniques are of limited utility in PMR due to the SNR regimes of the target-scattered and direct-path signals typical of the PMR signal environment.

Equivalent dipole source localization is a well-established approach to localizing the electrical activity in electroencephalogram (EEG). So far, source localization has been used primarily in localizing the epileptic source in human epileptic patients. Currently, source localization techniques have been applied to account for localizing epileptic source among the epileptic patients. Here, we present the first application of source localization in the field of sleep spindle in mouse brain. The spatial distribution of cortical potential was obtained by high density EEG and then the anterior and posterior sleep spindles were classified based on the K-mean clustering algorithm. To solve the forward problem, a realistic geometry brain model was produced based on boundary element method (BEM) using mouse MRI. Then, we applied four different source estimation algorithms (minimum norm, eLORETA, sLORETA, and LORETA) to estimate the spatial location of equivalent dipole source of sleep spindles. The estimated sources of anterior and posterior spindles were plotted in a cine-mode that revealed different topographic patterns of spindle propagation. The characterization of sleep spindles may be better be distinguished by our novel visualization method.

This paper deals with recent advances in acoustic experimental methods and especially acoustic imaging. The paper covers two areas of interest to acousticians. In the first part, it is explained how near-field acoustic holography (NAH) can be extended with beamforming in the near-field, focalization. The combination of the two methods is providing now a source localization solution with a good spatial resolution over the complete frequency range without the burden of measuring a large number of points as would be required if only NAH was used. In the second part of the paper, a method is described that goes one step beyond source localization. It is interesting to locate the noise sources, but from an engineering standpoint it is even more interesting to know the internal sources/forces causing these noise sources. In this part is explained how source localization techniques in conjunction with artificial excitation of the structure can provide information on the internal sources of the structure.

The accuracy of matched‐field techniques for source localization in a shallow‐water waveguide is dependent upon realistic modeling of the geoacoustic environment. A study was conducted to investigate the sensitivity of source localization to erroneous estimates of the geoacoustic properties (sound speed, density, and attenuation) of the sediment. A range‐independent normal‐mode computer program was used to calculate acoustic fields from a midwater source received on a vertical array of 21 hydrophones spanning the water column. Errors in estimates of range and depth are presented as a function of mismatch of each geoacoustic property.

Our goal is to determine the spatial origin of bioelectric activity within a peripheral nerve using recordings from a multi-contact nerve cuff electrode. This task is a bioelectric source localization problem, and a natural question is whether or not algorithms developed for the similar problem of EEG source localization can be applied in the peripheral nerve context. This technique requires a model of the nerve, and the present study aims to determine if using an idealized nerve geometry in the finite-element model is sufficient to achieve acceptable localization performance. Using a leadfield derived from the idealized model, the sLORETA algorithm was applied to recordings from a 56-channel “matrix” nerve cuff electrode placed around the rat sciatic nerve. The results show that the method was not able to reliably estimate the number and combination of active fascicles. A summary of the issues that need to be addressed before source localization techniques can be applied to peripheral nerves is provided.

Cortical source localization during facial emotion recognition in bipolar mania: An ERP study.

Location template matching (LTM) is a source localization technique in solids that is robust to dispersion and multipath. This is possible since LTM compares the input with a database of signals made at known locations. With this in place, it is possible to employ LTM in situations where the surface of interest takes an irregular shape. However, one of the existing LTM approaches uses crosscorrelation to compare the input and the database. It should be noted that if any two of the known locations stored in the database are too close, the cross-correlation method may have difficulties differentiating between signals generated from the neighboring points. To address this, we propose an algorithm which employs the linear predictive coding (LPC) that takes into account the dominant frequencies of a received signal. Using this approach, we show that the proposed algorithm is able to improve LTM's source localization accuracy under a real environment in the context of source localization for a touch interface.

Frequency analysis and topographic mapping of EEG and evoked potentials in epilepsy

The signal-to-noise ratio of real data is rarely known with complete certainty. However, Bayesian matched-field processing techniques for ocean acoustic source localization often require the signal-to-noise ratio (SNR) to be known a priori. In this paper, the effects of SNR mismatch on the performance of a Bayesian matched-field source localization method, the optimum uncertain field processor (A.M. Richardson and L.W. Nolte, J. Acoust. Soc. Am. vol.89, no.5, 2280-4 (1991)), are investigated. Theoretical and empirical analyses show that when the maximum a posteriori (MAP) estimate is utilized as the source location estimate, the localization performance is unaffected by the uncertainty regarding the SNR, provided that the assumed SNR is sufficiently high.

This paper proposes a robust technique for a sound source localization based on the time difference of arrival in noisy or reverberant environments. A nonlinear minimization problem for estimating source positions is formulated as a constrained optimization problem in consideration of room capacity. Then, a penalty function for a feasible region of this problem is used as the objective function. As a result, only estimation results within the room can be obtained. It is shown by several experiments that, by using the technique, estimation accuracy can be improved more than 25%.

Matched field processing (MFP) is an established technique for source localization in known multipath acoustic environments. Unfortunately, in many situations, particularly those involving high frequency signals, imperfect knowledge of the actual propagation environment prevents accurate propagation modeling and source localization via MFP fails. For beamforming applications, this actual-to-model mismatch problem was mitigated through a frequency downshift, made possible by a nonlinear array-signal-processing technique called frequency difference beamforming [Abadi, Song, and Dowling (2012). J. Acoust. Soc. Am. 132, 3018-3029]. Here, this technique is extended to conventional (Bartlett) MFP using simulations and measurements from the 2011 Kauai Acoustic Communications MURI experiment (KAM11) to produce ambiguity surfaces at frequencies well below the signal bandwidth where the detrimental effects of mismatch are reduced. Both the simulation and experimental results suggest that frequency difference MFP can be more robust against environmental mismatch than conventional MFP. In particular, signals of frequency 11.2 kHz-32.8 kHz were broadcast 3 km through a 106-m-deep shallow ocean sound channel to a sparse 16-element vertical receiving array. Frequency difference MFP unambiguously localized the source in several experimental data sets with average peak-to-side-lobe ratio of 0.9 dB, average absolute-value range error of 170 m, and average absolute-value depth error of 10 m.

It has been of recent interest to use autonomous underwater vehicles (AUVs) with towed arrays for source localization in ocean acoustic waveguides with uncertain environmental parameters. However, AUVs cannot implement source localization techniques designed to be robust against environmental mismatch because such techniques require more computational power than is typically available on an AUV. To address this issue, a technique is proposed that increases robustness to environmental mismatch by utilizing computational resources available before the measurement of the acoustic field is made (i.e., computational resources on the ship from which the AUV is launched). This is done by using a numerical ocean acoustic model to analyze how uncertainty in environmental parameters affects the acoustic pressure levels at the hydrophones of the array. The results of this analysis can then be stored on the AUV’s computer and used by the source localization algorithm. The relationship between the proposed technique and matched mode processing is discussed.

This paper presents beamforming techniques for source localization on aicraft in flight with a focus on the development at DLR in Germany. Fly-over tests with phased arrays are the only way to localize and analyze the different aerodynamic and engine sources of aircraft in flight. Many of these sources cannot be simulated numerically or in wind-tunnel tests because they they are either unknown or they cannot be resolved properly in model scale. The localization of sound sources on aircraft in flight is performed using large microphone arrays. For the data analysis, the source signals at emission time are reconstructed from the Doppler-shifted microphone data using the measured flight trajectory. Standard beamforming techniques in the frequency domain cannot be applied due transitory nature of the signals, so the data is usually analyzed using a classical beamforming algorithm in the time domain. The spatial resolution and the dynamic range of the source maps can be improved by calculating a deconvolution of the sound source maps with the point spread function of the microphone array. This compensates the imaging properties of the microphone array by eliminating side lobes and aliases. While classical beamfoming yields results that are more qualitative by nature, the deconvolution results can be used to integrate the acoustic power over the different source regions in order to obtain the powers of each source. ranking of the sources. These results can be used to rank the sources, for acoustic trouble shooting, and to assess the potential of noise abatement methods.