EEG Source Imaging: A Practical Review of the Analysis Steps

Abstract

The electroencephalogram (EEG) is one of the oldest technologies to measure neuronal activity of the human brain. It has its undisputed value in clinical diagnosis, particularly (but not exclusively) in the identification of epilepsy and sleep disorders and in the evaluation of dysfunctions in sensory transmission pathways. With the advancement of digital technologies, the analysis of EEG has moved from pure visual inspection of amplitude and frequency modulations over time to a comprehensive exploration of the temporal and spatial characteristics of the recorded signals. Today, EEG is accepted as a powerful tool to capture brain function with the unique advantage of measuring neuronal processes in the time frame in which these processes occur, namely in the sub-second range. However, it is generally stated that EEG suffers from a poor spatial resolution that makes it difficult to infer to the location of the brain areas generating the neuronal activity measured on the scalp. This statement has challenged a whole community of biomedical engineers to offer solutions to localize more precisely and more reliably the generators of the EEG activity. High-density EEG systems combined with precise information of the head anatomy and sophisticated source localization algorithms now exist that convert the EEG to a true neuroimaging modality. With these tools in hand and with the fact that EEG still remains versatile, inexpensive and portable, electrical neuroimaging has become a widely used technology to study the functions of the pathological and healthy human brain. However, several steps are needed to pass from the recording of the EEG to 3-dimensional images of neuronal activity. This review explains these different steps and illustrates them in a comprehensive analysis pipeline integrated in a stand-alone freely available academic software: Cartool. The information about how the different steps are performed in Cartool is only meant as a suggestion. Other EEG source imaging software may apply similar or different approaches to the different steps.

Keywords: EEG; head model; inverse model; pre-processing; source localization.

Figure 1

Illustration of the spatial filter implemented in Cartool. (A) Determination and removal of the maximal and minimal value of the 6 nearest neighbor of a given electrode. (B) Illustration of the waveforms and the map (C) at a given time point before filtering. (D) Illustration of the effect of the spatial filter on the waveforms and maps (E).

Figure 2

Illustration of the MRI processing pipeline. (A) Original anisotropic MRI. (B) Result of up-sampling and re-orientation, with red, green, and blue axis pointing, respectively to X, Y, Z. (C) Adjustment of the cutting planes and setting of the AC origin. (D) Result of the skull-stripping to isolate the brain. (E) Brain slices which exhibit the Bias Field of the original MRI. (F) Same brain slices post-Bias Field Correction. (G) Extraction of the Gray matter.

Figure 3

Illustration of the distribution of the solution points in the gray matter.

Figure 4

(A) Example of the location of 256 electrodes on the head determined by the artifacts that the electrodes create on the MRI image by wearing the EEG net in the scanner. (B) Location of the electrodes with respect to the brain: Blue: 256 electrode net. Red: Positions of the 19 electrodes of the standard clinical 10–20 system. The zoomed-in regions show the bad coverage of the frontal, basal temporal and midline areas with the 19 electrodes as compared to the 256 electrodes.

Figure 5

Original position of a template electrode layout with respect to the head of the subject (left) and the corrected positions after manual rotation and translation and the final automatic “gluing” on the scalp.

Figure 6

Illustration of the determination of the skull thickness under each electrode. A sagittal cutting plane is shown with the electrodes (in blue) located on the scalp surface, the radius lines (in yellow) extending from the center to each electrode, and on each line three dots showing where the skull and scalp limits are determined.

Figure 7

Age correction of skull thickness and skull conductivity. (A) Estimated average skull thickness across age. (B) Estimated skull conductivity ratios across age.

Figure 8

Source localization normalization. (A) Time series of 3 solution points showing the difference in mean amplitude (norm) between them. (B) The same time series as in (A) but after normalization, showing that the 3 solution points now have the same amplitude range. (C) Histograms of these 3 solution points, showing that the background activity is the left-most mode of the distribution. (D) Histogram after normalization, showing that all the background activity has been centered to 1. (E) Histograms for all solution points (vertical axis), with the red color coding for the highest source amplitude probability. (F) Histogram of source amplitude probability for each solution point after normalization, showing that all solution points now have a background range from 0 to 1, while retaining their respective highest activities.

Figure 9

Illustration of the actual vectorial results (top left, in 3D) of the distributed sources, and their corresponding amplitude values (top right, in 3D, and bottom as transverse slices).

Figure 10

Illustration of the visualization of the data and the results of the different analysis steps as implemented in Cartool. All windows can be independently manipulated in 3D. The screen shot shows a visual evoked potential (face presentation) recorded with 256 electrodes, the corresponding potential map at 188 ms post-stimulus and the estimated sources located in the mesial temporal lobes and the fusiform gyrus.