Source potential mapping: a new modality to image neural electric activities

A new method of spatio-temporal topographic mapping by correlation coefficient of K-means cluster

It would be of the utmost interest to map correlated sources in the working human brain by Event-Related Potentials (ERPs). This work is to develop a new method to map correlated neural sources based on the time courses of the scalp ERPs waveforms. The ERP data are classified first by k-means cluster analysis, and then the Correlation Coefficients (CC) between the original data of each electrode channel and the time course of each cluster centroid are calculated and utilized as the mapping variable on the scalp surface. With a normalized 4-concentric-sphere head model with radius 1, the performance of the method is evaluated by simulated data. CC, between simulated four sources (s (1)-s (4)) and the estimated cluster centroids (c (1)-c (4)), and the distances (Ds), between the scalp projection points of the s (1)-s (4) and that of the c (1)-c (4), are utilized as the evaluation indexes. Applied to four sources with two of them partially correlated (with maximum mutual CC = 0.4892), CC (Ds) between s (1)-s (4) and c (1)-c (4) are larger (smaller) than 0.893 (0.108) for noise levels NSR</= 0.2; Applied to four sources with two of them completely correlated, CC (Ds) between s (1)-s (4) and c (1)-c (4) are larger (smaller) than 0.97367 (0.1898) for a random noise level NSR</= 0.2; Applied to 128, 64 and 32 recording electrodes, CC (Ds) between s (1)-s (4) and c (1)-c (4) are larger (smaller) than 0.9557 (0.4251) for a random noise level NSR = 0.15; And applied to the cases of spatially overlapped scalp activities, CC (Ds) between s (1)-s (4) and c (1)-c (4) are larger (smaller) than 0.9083 (0.4329) for a random noise level NSR = 0.15. Finally, the method successfully decomposed the ERPs collected in a spatial selective attention experiment into three clusters located at left, right occipital and frontal. The estimated vectors of the contra-occipital area demonstrate that attention to the stimulus location produces increased amplitude of the P1 and N1 components over the contra-occipital scalp. The estimated vector in the frontal area displays two large processing negativity waves around 100 ms and 250 ms when subjects are attentive, and there is a small negative wave around 140 ms and a P300 when subjects are unattentive. The results of simulations and real Visual Evoked Potentials (VEPs) data demonstrate the validity of the method in mapping correlated sources. This method may be an objective, heuristic and important tool to study the properties of cerebral, neural networks in cognitive and clinical neurosciences.

High-resolution electroencephalogram (EEG) mapping: scalp charge layer

The neural electrical signal related to the human brain function is one of the tracks to understanding ourselves. Various electroencephalogram imaging techniques have been developed to reveal spatial information on neural activities in the brain from scalp recordings, such as Laplacian, equivalent source layer and potential. Physically, these methods may be classified into two categories: scalp surface or cortical surface based techniques. In this work, the focus is on the scalp surface based equivalent charge layer (ECL), with a comparison to the scalp potential with different references and scalp Laplacian (SL). The contents include theoretical analysis and numeric evaluation of simulated data and real alpha (8-12 Hz) data. The results confirm the fact that SL and ECL are of higher spatial resolution than various scalp potential maps, and for SL and ECL, SL is of higher resolution but more sensitive to noise.

High-resolution EEG mapping: an equivalent charge-layer approach

Brain electrical signal is one of the windows to understanding neural activities. Various high-resolution imaging techniques have been developed to reveal the electrical activities underneath the cortical surface from scalp electroencephalographic recordings, such as scalp Laplacian, cortical surface potential, equivalent charge layer (ECL) and equivalent dipole layer (EDL). In this work, we develop forward density formulae for the ECL and the EDL of neural electric sources in a 4-concentric-sphere head model, and compare ECL with EDL in theory, simulation and real evoked data tests. The results confirm that the ECL map may be of higher spatial resolution than the EDL map.

The theoretical relation of scalp Laplacian and scalp current density of a spherical shell head model

The theoretical relation between the scalp Laplacian (SL) and the scalp current density (SCD) is derived for a spherical shell head model. The result shows that they are related by a function of spatial frequency. For practically available low spatial frequencies, they are approximately linearly related to each other, so the SL estimate may be considered as an approximate SCD estimate in practice.

High-resolution EEG mapping: a radial-basis function based approach to the scalp Laplacian estimate

OBJECTIVES

The present study addressed a new scalp Laplacian mapping (LM) algorithm.

METHODS

Using a radial-basis function (RBF) as the interpolation basis function, and the smallest arc length on the surface of a spherical head model as the distance measure between two measurement sites, a new RBF based approach to LM is formulated.

RESULTS

With simulated data and empirical data, comparison between the new RBF based approach and the spherical spline function (SSF) based approach was conducted in a 4-concentric spheres head model, and the results show that the RBF based approach is better than the SSF based approach to LM.

CONCLUSIONS

The new RBF based approach to LM provides an additional efficient way for the neural electrical activities imaging.