Advanced Tools for Digital EEG Review: : Virtual Source Montages, Whole-head Mapping, Correlation, and Phase Analysis

Abstract We describe a comprehensive linear approach to the problem of imaging brain activity with high temporal as well as spatial resolution based on combining EEG and MEG data with anatomical constraints derived from MRI images. The "inverse problem" of estimating the distribution of dipole strengths over the cortical surface is highly underdetermined, even given closely spaced EEG and MEG recordings. We have obtained much better solutions to this problem by explicitly incorporating both local cortical orientation as well as spatial covariance of sources and sensors into our formulation. An explicit polygonal model of the cortical manifold is first constructed as follows: (1) slice data in three orthogonal planes of section (needle-shaped voxels) are combined with a linear deblurring technique to make a single high-resolution 3-D image (cubic voxels), (2) the image is recursively flood-filled to determine the topology of the gray-white matter border, and (3) the resulting continuous surface is refined by relaxing it against the original 3-D gray-scale image using a deformable template method, which is also used to computationally flatten the cortex for easier viewing. The explicit solution to an error minimization formulation of an optimal inverse linear operator (for a particular cortical manifold, sensor placement, noise and prior source covariance) gives rise to a compact expression that is practically computable for hundreds of sensors and thousands of sources. The inverse solution can then be weighted for a particular (averaged) event using the sensor covariance for that event. Model studies suggest that we may be able to localize multiple cortical sources with spatial resolution as good as PET with this technique, while retaining a much finer grained picture of activity over time.

Review and analysis of continuous EEG recordings may be impeded by physiological artifacts such as blinks, eye movements, or cardiac activity. Spatial filters based on artifact and brain signal topographies can remove artifacts completely without distortion of relevant brain activity. The authors describe the basic principle of artifact correction by spatial filtering and they review different approaches to estimate artifact and brain signal topographies. The main focus is on the preselection approach, which is fast enough to be applied while paging through the segments of a digital EEG recording. Examples of real EEG segments, containing epileptic seizure activity or interictal spikes contaminated by artifacts, show that spatial filtering by preselection can be a useful tool during EEG review. Advantages and disadvantages of the different spatial filter approaches are discussed.

A new type of EEG derivation has been investigated. This derivation, constituting a practical implementation of the Laplace operator, detects source activity as it appears at the surface level of the scalp. It is realized in the 10-20 system of electrode placement basically as an analogue superposition of four bipolar derivations, forming a star-like configuration around each electrode. Visual estimation of the topographical origins of a pattern, is thus replaced by a more efficient on-line process, which derives the source activity at the position of each individual electrode. Practical correlation tests have shown that the separation of adjacent derivations is improved by a factor of between two and four, compared to the corresponding bipolar and common reference derivations. Any feature of local origin will therefore have a correspondingly increased signal-to-noise ratio prior to the stage of visual or automatic interpretation. As a consequence of the partition of the scalp field into 19 source zreas, instead of utilizing an arbitrary number of potential differences, one fixed montage with 19 recorder channels is sufficient to present the total surface activity, within the limits of resolution of the electrode system.