Augmented reality-based electrode guidance system for reliable electroencephalography

Canine Electroencephalography Electrode Positioning Using a Neuronavigation System

BACKGROUND

Studies in people suggest that surface electroencephalography (EEG) electrode positions vary across participants and that the consistency of these positions is electrode-, region-, and examiner-dependent. The aim was to investigate the variability in EEG electrode positions to their underlying cortical regions (CRs) in dogs using a neuronavigation system and evaluate the use of said system in electrode positioning, via a cadaver study with 22 dogs. CT scans and MRI were performed for each dog. These were uploaded onto a neuronavigation system where the desired CRs were annotated. The electrode positions were marked on the heads, which were positioned using only a previously established guide and anatomical landmarks. Using the neuronavigation system, alignment or deviations from the desired CRs were noted. Fifty-three percent of all the marked electrode positions showed an alignment with the desired CRs. Thirty-three percent showed no alignment, and fourteen percent showed partial alignment. Three percent deviated to different cortical lobes. Placement via the neuronavigation system enabled reliable and replicable electrode positioning and CR alignment. The standard for EEG electrode placement in dogs is subjected to a high variance. A neuronavigation system can aid in more precise electrode placements. Specific gyri cannot accurately be evaluated on EEG without imaging-controlled electrode placement.

Neural silences can be localized rapidly using noninvasive scalp EEG

A rapid and cost-effective noninvasive tool to detect and characterize neural silences can be of important benefit in diagnosing and treating many disorders. We propose an algorithm, SilenceMap, for uncovering the absence of electrophysiological signals, or neural silences, using noninvasive scalp electroencephalography (EEG) signals. By accounting for the contributions of different sources to the power of the recorded signals, and using a hemispheric baseline approach and a convex spectral clustering framework, SilenceMap permits rapid detection and localization of regions of silence in the brain using a relatively small amount of EEG data. SilenceMap substantially outperformed existing source localization algorithms in estimating the center-of-mass of the silence for three pediatric cortical resection patients, using fewer than 3 minutes of EEG recordings (13, 2, and 11mm vs. 25, 62, and 53 mm), as well for 100 different simulated regions of silence based on a real human head model (12 ± 0.7 mm vs. 54 ± 2.2 mm). SilenceMap paves the way towards accessible early diagnosis and continuous monitoring of altered physiological properties of human cortical function.

The importance of modeling the human cerebral vasculature in blunt trauma

BACKGROUND

Multiple studies describing human head finite element (FE) models have established the importance of including the major cerebral vasculature to improve the accuracy of the model predictions. However, a more detailed network of cerebral vasculature, including the major veins and arteries as well as their branch vessels, can further enhance the model-predicted biomechanical responses and help identify correlates to observed blunt-induced brain injury.

METHODS

We used an anatomically accurate three-dimensional geometry of a 50th percentile U.S. male head that included the skin, eyes, sinuses, spine, skull, brain, meninges, and a detailed network of cerebral vasculature to develop a high-fidelity model. We performed blunt trauma simulations and determined the intracranial pressure (ICP), the relative displacement (RD), the von Mises stress, and the maximum principal strain. We validated our detailed-vasculature model by comparing the model-predicted ICP and RD values with experimental measurements. To quantify the influence of including a more comprehensive network of brain vessels, we compared the biomechanical responses of our detailed-vasculature model with those of a reduced-vasculature model and a no-vasculature model.

RESULTS

For an inclined frontal impact, the predicted ICP matched well with the experimental results in the fossa, frontal, parietal, and occipital lobes, with peak-pressure differences ranging from 2.4% to 9.4%. For a normal frontal impact, the predicted ICP matched the experimental results in the frontal lobe and lateral ventricle, with peak-pressure discrepancies equivalent to 1.9% and 22.3%, respectively. For an offset parietal impact, the model-predicted RD matched well with the experimental measurements, with peak RD differences of 27% and 24% in the right and left cerebral hemispheres, respectively. Incorporating the detailed cerebral vasculature did not influence the ICP but redistributed the brain-tissue stresses and strains by as much as 30%. In addition, our detailed-vasculature model predicted strain reductions by as much as 28% when compared to current reduced-vasculature FE models that only include the major cerebral vessels.

CONCLUSIONS

Our study highlights the importance of including a detailed representation of the cerebral vasculature in FE models to more accurately estimate the biomechanical responses of the human brain to blunt impact.

Using the MoBI motion capture system to rapidly and accurately localize EEG electrodes in anatomic space

During mobile brain/body imaging (MoBI) experiments, electroencephalography and motion capture systems are used in concert to record high temporal resolution neural activity and movement kinematics while participants perform demanding perceptual and cognitive tasks in a naturalistic environment. A typical MoBI setup involves positioning multi‐channel electrode caps based on anatomical fiducials as well as experimenter and participant intuition regarding the scalp midpoint location (i.e., Cz). Researchers often use the “template” electrode locations provided by the manufacturer, however, the “actual” electrode locations can vary based on each participant's head morphology. Accounting for differences in head morphologies could provide more accurate clinical diagnostic information when using MoBI to identify neurological deficits in patients with motor, sensory, or cognitive impairments. Here, we asked whether the existing motion capture system used in a MoBI setup could be easily adapted to improve spatial localization of electrodes across participants without requiring additional or specialized equipment that might impede clinical adoption. Using standard electrode configurations, infrared markers were placed on a subset of electrodes and anatomical fiducials, and the remaining electrode locations were estimated using spherical or ellipsoid models. We identified differences in event‐related potentials between “template” and “actual” electrode locations during a Go/No‐Go task (p < 9.8e–5) and an object‐manipulation task (p < 9.8e–5). Thus, the motion capture system already used in MoBI experiments can be effectively deployed to accurately register and quantify the neural activity. Improving the spatial localization without needing specialized hardware or additional setup time to the workflow has important real‐world implications for translating MoBI to clinical environments.

More Reliable EEG Electrode Digitizing Methods Can Reduce Source Estimation Uncertainty, but Current Methods Already Accurately Identify Brodmann Areas

Electroencephalography (EEG) and source estimation can be used to identify brain areas activated during a task, which could offer greater insight on cortical dynamics. Source estimation requires knowledge of the locations of the EEG electrodes. This could be provided with a template or obtained by digitizing the EEG electrode locations. Operator skill and inherent uncertainties of a digitizing system likely produce a range of digitization reliabilities, which could affect source estimation and the interpretation of the estimated source locations. Here, we compared the reliabilities of five digitizing methods (ultrasound, structured-light 3D scan, infrared 3D scan, motion capture probe, and motion capture) and determined the relationship between digitization reliability and source estimation uncertainty, assuming other contributors to source estimation uncertainty were constant. We digitized a mannequin head using each method five times and quantified the reliability and validity of each method. We created five hundred sets of electrode locations based on our reliability results and applied a dipole fitting algorithm (DIPFIT) to perform source estimation. The motion capture method, which recorded the locations of markers placed directly on the electrodes had the best reliability with an average electrode variability of 0.001 cm. Then, in order of decreasing reliability were the method using a digitizing probe in the motion capture system, an infrared 3D scanner, a structured-light 3D scanner, and an ultrasound digitization system. Unsurprisingly, uncertainty of the estimated source locations increased with greater variability of EEG electrode locations and less reliable digitizing systems. If EEG electrode location variability was ∽1 cm, a single source could shift by as much as 2 cm. To help translate these distances into practical terms, we quantified Brodmann area accuracy for each digitizing method and found that the average Brodmann area accuracy for all digitizing methods was >80%. Using a template of electrode locations reduced the Brodmann area accuracy to ∽50%. Overall, more reliable digitizing methods can reduce source estimation uncertainty, but the significance of the source estimation uncertainty depends on the desired spatial resolution. For accurate Brodmann area identification, any of the digitizing methods tested can be used confidently.

Using a structured-light 3D scanner to improve EEG source modeling with more accurate electrode positions

BACKGROUND

In this study, we evaluated the use of a structured-light 3D scanner for EEG electrode digitization. We tested its accuracy, robustness and evaluated its practical feasibility. Furthermore, we assessed how 3D scanning of EEG electrode positions affects the accuracy of EEG volume conduction models and source localization.

NEW METHOD

To assess the improvement in electrode positions and source results, we compared the electrode positions both at the scalp level and by quantifying source model accuracy between the 3D scanner, generic template, and cap-specific electrode positions.

RESULTS AND COMPARISON WITH EXISTING METHODS

The use of the 3D scanner significantly improves the accuracy of EEG electrode positions to a median error of 9.4 mm and maximal error of 32.8 mm, relative to the custom (median error of 10.9 mm, maximal error 39.1 mm) and manufacturer's template positions (median error of 13.8 mm, maximal error 57.0 mm). The relative difference measure (RDM) of the EEG source model averaged over the brain improves from 0.18 to 0.11. The dipole localization error averaged over the brain improves from 11.4 mm to 7.0 mm.

CONCLUSION

A structured-light 3D scanner improves the electrode position accuracy and thereby the EEG source model accuracy. It is more affordable than systems currently used for this, and allows for robust and fast digitization. Therefore, we consider it a cost and time-efficient way to improve EEG source reconstruction.