Findings about LORETA Applied to High-Density EEG—A Review

Electroencephalography (EEG) is a non-invasive diagnostic technique for recording brain electric activity. The EEG source localization has been an area of research widely explored during the last decades because it provides helpful information about brain physiology and abnormalities. Source localization consists in solving the so-called EEG inverse problem. Over the years, one of the most employed method for solving it has been LORETA (Low Resolution Electromagnetic Tomography). In particular, in this review, we focused on the findings about the LORETA family algorithms applied to high-density EEGs (HD-EEGs), used for improving the low spatial resolution deriving from the traditional EEG systems. The results were classified according to their clinical application and some aspects arisen from the analyzed papers were discussed. Finally, suggestions were provided for future improvement. In this way, the combination of LORETA with HD-EEGs could become an even more valuable tool for noninvasive clinical evaluation in the field of applied neuroscience

INDEPENDENT COMPONENT ANALYSIS AND DISCRETE WAVELET TRANSFORM FOR ARTIFACT REMOVAL IN BIOMEDICAL SIGNAL PROCESSING

Recent works have shown that artifact removal in bi omedical signals can be performed by using Discrete Wavelet Transform (DWT) or Independent Component Analysis (ICA). It results often very difficult to remove some artifacts because they could be superimposed on the recordings and they could corrupt the signals in the frequency domain. The two conditions could compromise the performance of both DWT and ICA methods. In this study we show that if the two methods are jointly implemented, it is possible to improve the performances for the artifact rejection procedure. We discuss in detail the new method and we also show how this method provides advantages with respect to DWT of ICA procedure. Finally, we tested the new approach on real data

Path Loss Prediction Using Fuzzy Inference System and Ellipsoidal Rules

It is well known as the prediction of radio wave path loss in urban environment plays a key role in order to correctly plan wireless systems and mobile communication networks. To obtain more flexible prediction models able to give accurate results, in recent years Soft computing Techniques has been exploited. In this study, a novel approach based on ellipsoidal fuzzy inference system EFIS is investigated. Results compared with those provided by the Okumura Hata model and the standard Fuzzy Inference System approach (FIS) show superior performances of the EFIS approach

An eLORETA Longitudinal Analysis of Resting State EEG Rhythms in Alzheimer’s Disease

Alzheimer’s disease (AD) is a degenerative brain disorder which is the most common cause of dementia. As there is no cure for AD, an early diagnosis is essential to slow down the progression of the disease with a proper pharmacological treatment. Electroencephalography (EEG) represents a valid tool for studying AD. EEG signals of AD patients are characterized by a “slowing”, meaning the power increases in low frequencies (delta and theta) and decreases in higher frequency (alpha and beta), compared to normal elderly. The purpose of our study is the computation of the power current density in eight patients, who were diagnosed with MCI at time T0 and mild AD at time T1 (four months later), starting from the brain active source reconstruction. The novelty is that we employed the eLORETA algorithm, unlike the previous studies which used the old version of the algorithm named LORETA. It is also the first longitudinal study which considers such a short time period to explore the evolution of the disease. Five patients out of eight showed an increasing power in delta and theta bands. Seven patients exhibited a lower activation in alpha 1 and beta 2 bands. Finally, six patients revealed a decreased power in alpha 2 and beta 1 bands. These findings are consistent with those reported in literature. On the other hand, the discrepancy of some outcome could be related to a not yet severe stage of the disease. In our opinion, this study could represent a good starting point for more detailed future investigation

Effect of Rehabilitation on Brain Functional Connectivity in a Stroke Patient Affected by Conduction Aphasia

Stroke is a medical condition that affects the brain and represents a leading cause of death and disability. Associated with drug therapy, rehabilitative treatment is essential for promoting recovery. In the present work, we report an EEG-based study concerning a left ischemic stroke patient affected by conduction aphasia. Specifically, the objective is to compare the brain functional connectivity before and after an intensive rehabilitative treatment. The analysis was performed by means of local and global efficiency measures related to the execution of three tasks: naming, repetition and reading. As expected, the results showed that the treatment led to a balancing of the values of both parameters between the two hemispheres since the rehabilitation contributed to the creation of new neural patterns to compensate for the disrupted ones. Moreover, we observed that for both name and repetition tasks, shortly after the stroke, the global and local connectivity are lower in the affected lobe (left hemisphere) than in the unaffected one (right hemisphere). Conversely, for the reading task, global and local connectivity are higher in the impaired lobe. This apparently contrasting trend can be due to the effects of stroke, which affect not only the site of structural damage but also brain regions belonging to a functional network. Moreover, changes in network connectivity can be task-dependent. This work can be considered a first step for future EEG-based studies to establish the most suitable connectivity measures for supporting the treatment of stroke and monitoring the recovery process

A Modified Heart Dipole Model for the Generation of Pathological ECG Signals

In this paper, we introduce a new dynamic model of simulation of electrocardiograms (ECGs) affected by pathologies starting from the well-known McSharry dynamic model for the ECGs without cardiac disorders. In particular, the McSharry model has been generalized (by a linear transformation and a rotation) for simulating ECGs affected by heart diseases verifying, from one hand, the existence and uniqueness of the solution and, on the other hand, if it admits instabilities. The results, obtained numerically by a procedure based on a Four Stage Lobatto IIIa formula, show the good performances of the proposed model in producing ECGs with or without heart diseases very similar to those achieved directly on the patients. Moreover, verified that the ECGs signals are affected by uncertainty and/or imprecision through the computation of the linear index and the fuzzy entropy index (whose values obtained are close to unity), these similarities among ECGs signals (with or without heart diseases) have been quantified by a well-established fuzzy approach based on fuzzy similarity computations highlighting that the proposed model to simulate ECGs affected by pathologies can be considered as a solid starting point for the development of synthetic pathological ECGs signals

Multivariate Multi-Scale Permutation Entropy for Complexity Analysis of Alzheimer’s Disease EEG

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