QEEG-Guided Neurofeedback: New Brain-Based Individualized Evaluation and Treatment for Autism By James Neubrander, MD, Michael Linden, PhD, Jay Gunkelman, QEEGD, and Cynthia Kerson, PhD ABSTRACT QEEG-guided neurofeedback is based on normalizing dysregulated brain regions that relate to specific clinical presentation. With ASD, this means that the approach is specific to each individual’s QEEG subtype patterns and presentation. The goal of neurofeedback with ASD is to correct amplitude abnormalities and balance brain functioning, while coherence neurofeedback aims to improve the connectivity and plasticity between brain regions. This tailored approach has implications that should not be underestimated. . . . Clinicians, including the authors, have had amazing results with ASD, including significant speech and communication improvements, calmer and less aggressive behavior, increased attention, better eye contact, and improved socialization. Many of our patients have been able to reduce or eliminate their medications after completion of QEEG-guided neurofeedback. View Publication
Functional Imaging based on swLORETA and phase synchronization Ernesto Palmero Soler ABSTRACT The tomographic properties of swLORETA and sLORETA were compared using both simulated and real data. In the simulation studies, the reconstruction of single and multiple current dipoles was simulated varying their position and orientation across the source space and taking into account the presence of noise. The real data was obtained from healthy subjects who performed a classical spatial attention experiment. The tests performed demonstrated that the resulting algorithm is not only efficient but also accurate as demonstrated by the analysis of a spatial attention experiment. View Publication
swLORETA: a novel approach to robust source localization and synchronization tomography Ernesto Palmero-Soler, Kevin Dolan, Volker Hadamschek, and Peter A Tass ABSTRACT Standardized low-resolution brain electromagnetic tomography (sLORETA) is a widely used technique for source localization. However, this technique still has some limitations, especially under realistic noisy conditions and in the case of deep sources. To overcome these problems, we present here swLORETA, an improved version of sLORETA, obtained by incorporating a singular value decomposition-based lead field weighting. We show that the precision of the source localization can further be improved by a tomographic phase synchronization analysis based on swLORETA. The phase synchronization analysis turns out to be superior to a standard linear coherence analysis, since the latter cannot distinguish between real phase locking and signal mixing. View Publication