Younes

Higher Resolution sLORETA (HR-sLORETA) in EEG Source Imaging
sLORETA is one of the well-established EEG source localization methods that is popular for its satisfactory estimation, simplicity, and fast computation. However, the method has a low-resolution and requires manual postprocessing thresholding to provide a sparser solution with acceptable resolution in source detection. Here we propose a subspace based thresholding that results in a higher resolution brain imaging based on minimizing a desired least square source detection error. Simulation results show the proposed method, denoted by HR-sLORETA, provides stable and high resolution solution in terms of Percentage of Undetected Sources (PUS) and Spatial Dispersion (SD) compared to the existing manual thresholding approaches as well as Otsu thresholding approach. It is shown that HR-sLORETA outperforms Otsu, which is the only other available automatic thresholding method, in scenarios with three or more sources

Efficient Blinking Component Estimation in Subspace-Based EEG and MEG Analysis
Removing EyeBlink (EB) artifact is often an essential preprocessing step of EEG/MEG analysis. Subspace-based methods such as Independent Component Analysis (ICA) and Signal Source Projection (SSP) are well-known methods of EB artifact removal. However, these methods require preknowledge of the number of components (NoC) involved in the blinking segments which is unknown and estimated by visual inspection. Here, we provide a reliable and efficient method for estimating the NoC of EB. The method utilizes an SVD denoising approach denoted by Number of Source Eigenvalue Error (NoSEE). The proposed method performance is examined by synthetic EEG, and is validated by real MEG data. Root Mean Square Error (RMSE) is used to evaluate the performance while visual inspection on real MEG data is used to validate the algorithms results. In both cases, the proposed method provides optimum NoC in the sense of MSE minimization and consistency with the real data. The method provides more accurate results compared to manual methods. In addition, the low computational complexity of the algorithm results in much less processing time for this automatic NoC calculator compared to the trial and error procedure of the manual methods which enables the algorithm to be used in online EEG analysis.

Automated EEG Source Error Thresholding (AESET) in L2-Regularization Inverse Problems
Brain source localization techniques aim to provide high spatial resolution for EEG data which are known to have a high temporal resolution. Yet, this task is very challenging and limited due to the influence of volume conduction effect. Most popular inverse solutions which provide a reasonable estimate for the location of the source while being simple and computationally fast, such as the L2 -Regularization based category of solutions, tends to provide low resolution and blurred estimated. Here we present a probabilistic approach for automating the thresholding of the L2-based inverse solutions that will result in a more reliable solution. The approach utilizes Minimum Noiseless Description Length (MNDL) thresholding. Addition of this method to the result of the inverse solutions confines the need for manual thresholding. The method‘s performance is evaluated in a series of synthetic simulations with the change in parameters such as Signal-to-Noise Ratio (SNR) and the number of source patches. Mean Square Error (MSE) and Percentage of Undetermined Source (PUS) are used to evaluate the performance of the thresholding method, and the results illustrate the advantage of this automatic thresholding over the manual thresholding.