Source Localization Model Research Articles

P094 Method for EEG guided transcranial Electrical Stimulation without models

Objective There is a long interest in using EEG measurements to inform transcranial Electrical Stimulation (tES) but adoption is lacking. The conventional approach is to use anatomical head-models for both source localization (the EEG inverse problem) and current flow modeling (the tES forward model), but this approach is computationally demanding, requires an anatomical MRI, and strict assumptions about the target brain regions. We evaluate techniques whereby tES dose is derived from EEG without the need for an anatomical head model or assumptions. Approach The approaches are verified using a Finite Element Method (FEM) simulation of the EEG generated by a dipole, oriented either tangential or radial to the surface, and then simulating brain current flow produced by various model-free techniques including: (1) Voltage-to-voltage, (2) Voltage-to-Current; (3) Laplacian; and two Ad-Hoc techniques (4) Dipole sink-to-sink; and (5) Sink to Concentric Ring. These model-free approaches are compared to a numerically optimized dose that assumes perfect understanding of the dipole location and head anatomy. We vary the number of electrodes from a few to over three hundred, with focality or intensity as optimization criterion. Main results Our results demonstrate how simple Ad-Hoc approaches can achieve reasonable targeting for the case of a cortical dipole with 2–8 electrodes and no need for a model of the head. Significance For its simplicity and linearity, model-free EEG guided lends itself to broad adoption and can be applied to a static (tDCS), time-variant (e.g. tACS, tRNS, tPCS), or closed-loop tES. Figure options Download full-size image Download high-quality image (1118 K) Download as PowerPoint slide Figure options Download full-size image Download high-quality image (1499 K) Download as PowerPoint slide

The Brain Network Underpinning Novel Melody Creation.

Musical improvisation offers an excellent experimental paradigm for the study of real-time human creativity. It involves moment-to-moment decision-making, monitoring of one's performance, and utilizing external feedback to spontaneously create new melodies or variations on a melody. Recent neuroimaging studies have begun to study the brain activity during musical improvisation, aiming to unlock the mystery of human creativity. What brain resources come together and how these are utilized during musical improvisation are not well understood. To help answer these questions, we recorded electroencephalography (EEG) signals from 19 experienced musicians while they played or imagined short isochronous learned melodies and improvised on those learned melodies. These four conditions (Play-Prelearned, Play-Improvised, Imagine-Prelearned, Imagine-Improvised) were randomly interspersed in a total of 300 trials per participant. From the sensor-level EEG, we found that there were power differences in the alpha (8-12 Hz) and beta (13-30 Hz) bands in separate clusters of frontal, parietal, temporal, and occipital electrodes. Using EEG source localization and dipole modeling methods for task-related signals, we identified the locations and network activities of five sources: the left superior frontal gyrus (L SFG), supplementary motor area (SMA), left inferior parietal lobule (L IPL), right dorsolateral prefrontal cortex, and right superior temporal gyrus. During improvisation, the network activity between L SFG, SMA, and L IPL was significantly less than during the prelearned conditions. Our results support the general idea that attenuated cognitive control facilitates the production of creative output.

The Algebro-geometric Study of Range Maps

Localizing a radiant source is a widespread problem to many scientific and technological research areas. E.g. localization based on range measurements stays at the core of technologies like radar, sonar and wireless sensors networks. In this manuscript we study in depth the model for source localization based on range measurements obtained from the source signal, from the point of view of algebraic geometry. In the case of three receivers, we find unexpected connections between this problem and the geometry of Kummer's and Cayley's surfaces. Our work gives new insights also on the localization based on range differences.

A simple method for EEG guided transcranial electrical stimulation without models

Objective. There is longstanding interest in using EEG measurements to inform transcranial Electrical Stimulation (tES) but adoption is lacking because users need a simple and adaptable recipe. The conventional approach is to use anatomical head-models for both source localization (the EEG inverse problem) and current flow modeling (the tES forward model), but this approach is computationally demanding, requires an anatomical MRI, and strict assumptions about the target brain regions. We evaluate techniques whereby tES dose is derived from EEG without the need for an anatomical head model, target assumptions, difficult case-by-case conjecture, or many stimulation electrodes. Approach. We developed a simple two-step approach to EEG-guided tES that based on the topography of the EEG: (1) selects locations to be used for stimulation; (2) determines current applied to each electrode. Each step is performed based solely on the EEG with no need for head models or source localization. Cortical dipoles represent idealized brain targets. EEG-guided tES strategies are verified using a finite element method simulation of the EEG generated by a dipole, oriented either tangential or radial to the scalp surface, and then simulating the tES-generated electric field produced by each model-free technique. These model-free approaches are compared to a ‘gold standard’ numerically optimized dose of tES that assumes perfect understanding of the dipole location and head anatomy. We vary the number of electrodes from a few to over three hundred, with focality or intensity as optimization criterion. Main results. Model-free approaches evaluated include (1) voltage-to-voltage, (2) voltage-to-current; (3) Laplacian; and two Ad-Hoc techniques (4) dipole sink-to-sink; and (5) sink to concentric. Our results demonstrate that simple ad hoc approaches can achieve reasonable targeting for the case of a cortical dipole, remarkably with only 2–8 electrodes and no need for a model of the head. Significance. Our approach is verified directly only for a theoretically localized source, but may be potentially applied to an arbitrary EEG topography. For its simplicity and linearity, our recipe for model-free EEG guided tES lends itself to broad adoption and can be applied to static (tDCS), time-variant (e.g., tACS, tRNS, tPCS), or closed-loop tES.

Cortical Dynamic Causality Network for Auditory-Motor Tasks

Motor preparation and execution require the interactions of a large-scale brain network, while the study of the dynamic changes of their interactions could uncover the underlying neural mechanism of the corresponding information processing. This dynamic analysis requires high temporal resolution of the recorded signals. Electroencephalogram (EEG) with high temporal resolution has been widely used in related studies. However, studies based on scalp EEG always lead to distorted results, due to scalp volume conduction, compared with that of cortically recorded signals. In the current study, the dynamic networks of motor preparation and execution are investigated using Go/No-go tasks performed with the left/right hand. In the analysis, the EEG source localization and dynamic causal model are combined together to investigate the neural processes of motor preparation and execution. The results show that similar network patterns with nodes distributed in the bilateral occipital lobe, bilateral temporal lobe, bilateral dorsolateral prefrontal cortex, and contralateral supplementary motor area could be revealed for both the Go and No-go tasks. Statistical testing further indicates that stronger couplings with the supplementary motor area could be found in Go and right-hand response tasks compared with No-go and left-hand response tasks, respectively. The findings in the current study demonstrate that the information exchange within the motor related brain networks plays an important role for motor related functions, i.e., the different motor functions may have the different information exchange and processing network patterns.

The New York Head—A precise standardized volume conductor model for EEG source localization and tES targeting

In source localization of electroencephalograpic (EEG) signals, as well as in targeted transcranial electric current stimulation (tES), a volume conductor model is required to describe the flow of electric currents in the head. Boundary element models (BEM) can be readily computed to represent major tissue compartments, but cannot encode detailed anatomical information within compartments. Finite element models (FEM) can capture more tissue types and intricate anatomical structures, but with the higher precision also comes the need for semi-automated segmentation, and a higher computational cost. In either case, adjusting to the individual human anatomy requires costly magnetic resonance imaging (MRI), and thus head modeling is often based on the anatomy of an ‘arbitrary’ individual (e.g. Colin27). Additionally, existing reference models for the human head often do not include the cerebro-spinal fluid (CSF), and their field of view excludes portions of the head and neck—two factors that demonstrably affect current-flow patterns. Here we present a highly detailed FEM, which we call ICBM-NY, or "New York Head". It is based on the ICBM152 anatomical template (a non-linear average of the MRI of 152 adult human brains) defined in MNI coordinates, for which we extended the field of view to the neck and performed a detailed segmentation of six tissue types (scalp, skull, CSF, gray matter, white matter, air cavities) at 0.5mm3 resolution. The model was solved for 231 electrode locations. To evaluate its performance, additional FEMs and BEMs were constructed for four individual subjects. Each of the four individual FEMs (regarded as the ‘ground truth’) is compared to its BEM counterpart, the ICBM-NY, a BEM of the ICBM anatomy, an ‘individualized’ BEM of the ICBM anatomy warped to the individual head surface, and FEMs of the other individuals. Performance is measured in terms of EEG source localization and tES targeting errors. Results show that the ICBM-NY outperforms FEMs of mismatched individual anatomies as well as the BEM of the ICBM anatomy according to both criteria. We therefore propose the New York Head as a new standard head model to be used in future EEG and tES studies whenever an individual MRI is not available. We release all model data online at neuralengr.com/nyhead/ to facilitate broad adoption.

A Pulse‐Type Hardware Extremal Nucleus of the Inferior Colliculus Model for 2D Sound Source Localization Based on the Auditory Sense Mechanism of the Barn Owl

SUMMARYHearing information is very important when visual information is limited. Sound source localization has been noted as particularly important in hearing information processing. Barn owls have excellent auditory processing. We propose a hardware model based on the auditory sense mechanism of barn owls. In this paper, we consider a model of the external nucleus of the inferior colliculus (ICx), which locates the source of a sound by the interaural time difference (ITD) for localization in the horizontal direction, and the interaural level difference (ILD) for localization in the vertical direction. The results show that the detection position of an ITD model and the detection position of an ILD model can converge. In addition, the output position of the ICx model changes with the position of the sound source.

Outlier identification for TOA-based source localization in the presence of noise

Although source localization has attracted great deal interest from the scientific community, the subject of localization in the presence of outliers has yet to receive proper attention. In many real life applications, some of the measurements are corrupted by outliers which may cause large estimation errors. Therefore, it is important to identify those outliers in order to remove the corrupted measurements from the data. Although known methods for outliers detection achieve good results, their complexity is usually high. We rely on sparse methods in order to identify and remove the outliers and obtain improved localization accuracy. We present models of Time-Of-Arrival (TOA) measurements affected by outliers and noise. We use the ℓ1 norm as a penalty function. Linear programming (LP) with the addition of threshold is used in order to detect the outliers. We prove that despite the additive noise, perfect detection of the outliers is possible when the amplitudes of the outliers are larger than a given value. The main contribution of this work is the development of a simple efficient method for outlier detection in source localization models when additive noise is present.

A Pulse‐Type Hardware Level Difference Detection Model Based on Sound Source Localization Mechanism in the Barn Owl

SUMMARYAuditory information processing is very important in the darkness where vision information is extremely limited. Barn owls have an excellent hearing information processing function. Barn owls can detect a sound source with high accuracy to within less than two degrees in both the vertical and horizontal directions. When they perform sound source localization, barn owls use the interaural time difference for localization in the horizontal plane, and the interaural level difference for localization in the vertical plane. We are constructing a two‐dimensional sound source localization model using pulse‐type hardware neuron models based on sound source the localization mechanism of barn owl for the purpose of engineering application. In this paper we propose a pulse‐type hardware model for level difference detection based on sound source the localization mechanism of the barn owl. First, we discuss the response characteristics of the mathematical model for level difference detection. Next we discuss the response characteristics of the hardware mode. The results show clearly that this proposed model can be used as a sound source localization model for the vertical direction.

Circle Fitting Using a Virtual Source Localization Algorithm in Wireless Sensor Networks

A novel circle fitting algorithm is proposed in this paper. The key points of this paper are given as follows: (i) it formulates the circle fitting problem into the special source localization one in wireless sensor networks (WSN); (ii) the multidimensional scaling (MDS) analysis is applied to the data points, and thus the propagator-like method is proposed to represent the circle center parameters as the functions of the circle radius; (iii) the virtual source localization model can be rerepresented as special nonlinear equations of a unique variable (the circle radius) rather than the original three ones (the circle center and radius), and thus the classical fixed-point iteration algorithm is applied to determine the radius and the circle center parameters. The effectiveness of the proposed circle fitting approach is demonstrated using the simulation and experimental results.

Evidence for Opponent Process Analysis of Sound Source Location in Humans

Research with barn owls suggested that sound source location is represented topographically in the brain by an array of neurons each tuned to a narrow range of locations. However, research with small-headed mammals has offered an alternative view in which location is represented by the balance of activity in two opponent channels broadly tuned to the left and right auditory space. Both channels may be present in each auditory cortex, although the channel representing contralateral space may be dominant. Recent studies have suggested that opponent channel coding of space may also apply in humans, although these studies have used a restricted set of spatial cues or probed a restricted set of spatial locations, and there have been contradictory reports as to the relative dominance of the ipsilateral and contralateral channels in each cortex. The current study used electroencephalography (EEG) in conjunction with sound field stimulus presentation to address these issues and to inform the development of an explicit computational model of human sound source localization. Neural responses were compatible with the opponent channel account of sound source localization and with contralateral channel dominance in the left, but not the right, auditory cortex. A computational opponent channel model reproduced every important aspect of the EEG data and allowed inferences about the width of tuning in the spatial channels. Moreover, the model predicted the oft-reported decrease in spatial acuity measured psychophysically with increasing reference azimuth. Predictions of spatial acuity closely matched those measured psychophysically by previous authors.

Distributed Asymptotic Minimization of Sequences of Convex Functions by a Broadcast Adaptive Subgradient Method

We propose a non-hierarchical decentralized algorithm for the asymptotic minimization of possibly time-varying convex functions. In our method, each agent in a network has a private, local (possibly time-varying) cost function, and the objective is to minimize asymptotically the sum of these local functions in every agent (this problem appears in many different applications such as, among others, motion planning, acoustic source localization, and environmental modeling). The algorithm consists of two main steps. First, to improve the estimate of a minimizer, agents apply a particular version of the adaptive projected subgradient method to their local functions. Then the agents exchange and mix their estimates using a communication model based on recent results of consensus algorithms. We show formally the convergence of the resulting scheme, which reproduces as particular cases many existing methods such as gossip consensus algorithms and recent decentralized adaptive subgradient methods (which themselves include as particular cases many distributed adaptive filtering algorithms). To illustrate two possible applications, we consider the problems of acoustic source localization and environmental modeling via network gossiping with mobile agents.

P6-11 Comparison of dipole analysis and functional MRI in localization of epileptiform discharges

Objective: To test the relative accuracy of two different strategies for source localization of interictal spikes. Methods: 20 patients with intractable epilepsy underwent surgery for epileptogenic focus resection. Digital EEG (21 channels 10 20 & T1T2) recorded before surgery was analyzed. Spikes were automatically searched and classified according to morphology and topography, visually screened and averaged. A dipole fitting method (BESA) and a distributed source model (eLORETA) for source localization of interictal spikes was applied to the same data. Results: Both BESA and eLORETA located sources only within the resected area (RA) in 12 patients. BESA located multiple sources, most of them inside RA with few outside RA in 2 patients and eLORETA in 4. Both methods found sources predominantly outside RA, with still some within RA in three patients. BESA showed sources only outside RA in 3 patients and eLORETA in only one. The two methods gave discordant results in 5 patients. The best match to the RA was found in patients with mesiotemporal lobe epilepsy. When analyzing the 37 independent spike clusters found across the 20 patients, BESA located the spike within RA in 22 (59.5%), in the close vicinity (same lobe) in 2, in contralateral homologous areas in 4, in another lobe but still near RA in another 5, and elsewhere in 4. Localization with eLORETA gave sources within RA in 25 cases (67%), in the same lobe in two, in contralateral homologous areas in 4, in another lobe near RA in 4 and elsewhere in 2. Discordances in localization between both methods were found for just 5 of the 37 spikes (p < 0.001). Conclusions: Both BESA and LORETA localized most interictal spikes within the resected area. Some localization discordance was found, and the best match with clinical resection decisions was achieved when using both methods together.

P6-13 Ceftazidime induced non-convulsive status epilepticus in a uremic and stroke patient

Objective: To test the relative accuracy of two different strategies for source localization of interictal spikes. Methods: 20 patients with intractable epilepsy underwent surgery for epileptogenic focus resection. Digital EEG (21 channels 10 20 & T1T2) recorded before surgery was analyzed. Spikes were automatically searched and classified according to morphology and topography, visually screened and averaged. A dipole fitting method (BESA) and a distributed source model (eLORETA) for source localization of interictal spikes was applied to the same data. Results: Both BESA and eLORETA located sources only within the resected area (RA) in 12 patients. BESA located multiple sources, most of them inside RA with few outside RA in 2 patients and eLORETA in 4. Both methods found sources predominantly outside RA, with still some within RA in three patients. BESA showed sources only outside RA in 3 patients and eLORETA in only one. The two methods gave discordant results in 5 patients. The best match to the RA was found in patients with mesiotemporal lobe epilepsy. When analyzing the 37 independent spike clusters found across the 20 patients, BESA located the spike within RA in 22 (59.5%), in the close vicinity (same lobe) in 2, in contralateral homologous areas in 4, in another lobe but still near RA in another 5, and elsewhere in 4. Localization with eLORETA gave sources within RA in 25 cases (67%), in the same lobe in two, in contralateral homologous areas in 4, in another lobe near RA in 4 and elsewhere in 2. Discordances in localization between both methods were found for just 5 of the 37 spikes (p < 0.001). Conclusions: Both BESA and LORETA localized most interictal spikes within the resected area. Some localization discordance was found, and the best match with clinical resection decisions was achieved when using both methods together.

P6-10 Value of dipole analysis and eLORETA in the localization of EEG interictal spikes in epilepsy surgery patients

Objective: To test the relative accuracy of two different strategies for source localization of interictal spikes. Methods: 20 patients with intractable epilepsy underwent surgery for epileptogenic focus resection. Digital EEG (21 channels 10 20 & T1T2) recorded before surgery was analyzed. Spikes were automatically searched and classified according to morphology and topography, visually screened and averaged. A dipole fitting method (BESA) and a distributed source model (eLORETA) for source localization of interictal spikes was applied to the same data. Results: Both BESA and eLORETA located sources only within the resected area (RA) in 12 patients. BESA located multiple sources, most of them inside RA with few outside RA in 2 patients and eLORETA in 4. Both methods found sources predominantly outside RA, with still some within RA in three patients. BESA showed sources only outside RA in 3 patients and eLORETA in only one. The two methods gave discordant results in 5 patients. The best match to the RA was found in patients with mesiotemporal lobe epilepsy. When analyzing the 37 independent spike clusters found across the 20 patients, BESA located the spike within RA in 22 (59.5%), in the close vicinity (same lobe) in 2, in contralateral homologous areas in 4, in another lobe but still near RA in another 5, and elsewhere in 4. Localization with eLORETA gave sources within RA in 25 cases (67%), in the same lobe in two, in contralateral homologous areas in 4, in another lobe near RA in 4 and elsewhere in 2. Discordances in localization between both methods were found for just 5 of the 37 spikes (p < 0.001). Conclusions: Both BESA and LORETA localized most interictal spikes within the resected area. Some localization discordance was found, and the best match with clinical resection decisions was achieved when using both methods together.

Variable Anisotropic Brain Electrical Conductivities in Epileptogenic Foci

Source localization models assume brain electrical conductivities are isotropic at about 0.33 S/m. These assumptions have not been confirmed ex vivo in humans. This study determined bidirectional electrical conductivities from pediatric epilepsy surgery patients. Electrical conductivities perpendicular and parallel to the pial surface of neocortex and subcortical white matter (n = 15) were measured using the 4-electrode technique and compared with clinical variables. Mean (±SD) electrical conductivities were 0.10 ± 0.01 S/m, and varied by 243% from patient to patient. Perpendicular and parallel conductivities differed by 45%, and the larger values were perpendicular to the pial surface in 47% and parallel in 40% of patients. A perpendicular principal axis was associated with normal, while isotropy and parallel principal axes were linked with epileptogenic lesions by MRI. Electrical conductivities were decreased in patients with cortical dysplasia compared with non-dysplasia etiologies. The electrical conductivity values of freshly excised human brain tissues were approximately 30% of assumed values, varied by over 200% from patient to patient, and had erratic anisotropic and isotropic shapes if the MRI showed a lesion. Understanding brain electrical conductivity and ways to non-invasively measure them are probably necessary to enhance the ability to localize EEG sources from epilepsy surgery patients.

Neural “Ignition”: Enhanced Activation Linked to Perceptual Awareness in Human Ventral Stream Visual Cortex

Human recognition performance is characterized by abrupt changes in perceptual states. Understanding the neuronal dynamics underlying such transitions could provide important insights into mechanisms of recognition and perceptual awareness. Here we examined patients monitored for clinical purposes with multiple subdural electrodes. The patients participated in a backward masking experiment in which pictures of various object categories were presented briefly followed by a mask. We recorded ECoG from 445 electrodes placed in 11 patients. We found a striking increase in gamma power (30-70 Hz) and evoked responses specifically associated with successful recognition. The enhanced activation occurred 150-200 ms after stimulus onset and consistently outlasted the stimulus presentation. We propose that the gamma and evoked potential activations reflect a rapid increase in recurrent neuronal activity that plays a critical role in the emergence of a recognizable visual percept in conscious awareness.

Implementation of a Hebbian chemoreceptor model for diffusive source localization

While new approaches to chemical localization have been proposed, animals are still widely used for locating landmines and illegal substances. Existing electronic noses still do not have the necessary sensitivity and accuracy. By modeling a cell’s chemical detection system, we can gain insight into the basic “olfactory” system. We use an inspiration from chemotaxis and Hebbian learning to enhance localization and tracking of gradient sources, which can be applied to both chemicals and heat. The eukaryotic receptor clustering model shows improvement over previous prokaryotic chemotaxis-inspired methods that do not take into account receptor clustering. Receptor clustering essentially adapts receptors spatio-temporally. For a mobile simulation, our method locates the source in less convergence time than the other chemotaxis algorithms and insignificantly less time compared to no spatio-temporal filtering (e.g. a single-sensor memoryless case). We then show that local regions of receptor cooperation have the best performance reflecting observations of receptor behavior in biology. To demonstrate the performance of this system in real-time, a stationary 4/8-sensor version of the array is implemented, and the algorithm improves the convergence time, mean, and variance of the Direction-of-Arrival calculation in diffusive, turbulent, and noisy environments.

A Semidefinite Programming Approach to Source Localization in Wireless Sensor Networks

We propose a novel approach to the source localization and tracking problem in wireless sensor networks. By applying minimax approximation and semidefinite relaxation, we transform the traditionally nonlinear and nonconvex problem into convex optimization problems for two different source localization models involving measured distance and received signal strength. Based on the problem transformation, we develop a fast low-complexity semidefinite programming (SDP) algorithm for two different source localization models. Our algorithm can either be used to estimate the source location or be used to initialize the original nonconvex maximum likelihood algorithm.

Directionally Constrained Filterbank ICA

A modification is proposed to the independent component analysis (ICA)-based filterbank approach in consideration to its structural similarity with binaural auditory model of sound source localization. The estimated sound locations provide an additional cue to the learning algorithm, which is utilized for initialization and imposition of directional constraints on the subband separation networks. Directionally constrained filterbank ICA (DC-FBICA) gives faster convergence and improves separation performance for noisy mixtures having significant spectral overlap among the convolved mixture and the corrupting noise. However, only slight improvement in separation performance is observed when the additive noise is a low frequency noise, although faster convergence is still observed.