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.

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Non-invasive EEG source localization using particle swarm optimization: A clinical experiment

2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society ◽

10.1109/embc.2012.6347418 ◽

2012 ◽

Cited By ~ 7

Author(s):

Y. Shirvany ◽

F. Edelvik ◽

S. Jakobsson ◽

A. Hedstrom ◽

Q. Mahmood ◽

...

Keyword(s):

Particle Swarm Optimization ◽

Source Localization ◽

Particle Swarm ◽

Swarm Optimization ◽

Non Invasive ◽

Eeg Source Localization ◽

Clinical Experiment

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Electroencephalography-Based Source Localization for Depression Using Standardized Low Resolution Brain Electromagnetic Tomography – Variational Mode Decomposition Technique

European Neurology ◽

10.1159/000500414 ◽

2019 ◽

Vol 81 (1-2) ◽

pp. 63-75

Author(s):

Chamandeep Kaur ◽

Preeti Singh ◽

Sukhtej Sahni

Keyword(s):

Inverse Problem ◽

Source Localization ◽

Variational Mode Decomposition ◽

Eeg Signals ◽

Low Resolution ◽

Delay In Diagnosis ◽

Electromagnetic Tomography ◽

Brain Electromagnetic ◽

Mode Decomposition ◽

Eeg Source Localization

Background: Electroencephalography (EEG) may be used as an objective diagnosis tool for diagnosing various disorders. Recently, source localization from EEG is being used in the analysis of real-time brain monitoring applications. However, inverse problem reduces the accuracy in EEG signal processing systems. Objectives: This paper presents a new method of EEG source localization using variational mode decomposition (VMD) and standardized the low resolution brain electromagnetic tomography (sLORETA) inverse model. The focus is to compare the effectiveness of the proposed approach for EEG signals of depression patients. Method: As the first stage, real EEG recordings corresponding to depression patients are decomposed into various mode functions by applying VMD. Then, closely related functions are analyzed using the inverse modelling-based source localization procedures such as sLORETA. Simulations have been carried out on real EEG databases for depression to demonstrate the effectiveness of the proposed techniques. Results: The performance of the algorithm has been assessed using localization error (LE), mean square error and signal to noise ratio output corresponding to simulated EEG dipole sources and real EEG signals for depression. In order to study the spatial resolution for cortical potential distribution, the main focus has been on studying the effects of noise sources and estimating LE of inverse solutions. More accurate and robust localization results show that this methodology is very promising for EEG source localization of depression signals. Conclusion: It can be said that proposed algorithm efficiently suppresses the influence of noise in the EEG inverse problem using simulated EEG activity and EEG database for depression. Such a system may offer an effective solution for clinicians as a crucial stage of EEG pre-processing in automated depression detection systems and may prevent delay in diagnosis.

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Robust EEG Source Localization Using Subspace Principal Vector Projection Technique

2020 28th European Signal Processing Conference (EUSIPCO) ◽

10.23919/eusipco47968.2020.9287482 ◽

2021 ◽

Author(s):

Amita Giri ◽

Lalan Kumar ◽

Tapan Gandhi

Keyword(s):

Source Localization ◽

Projection Technique ◽

Principal Vector ◽

Vector Projection ◽

Eeg Source Localization

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EVALUATION OF NON-INVASIVE MEASUREMENT OF ELECTROPHYSIOLOGICAL "HV" INTERVAL USING A HIGH-DENSITY AND HIGH-FIDELITY SIGNAL AVERAGING ECG DEVICE

Case Medical Research ◽

10.31525/ct1-nct04138797 ◽

2019 ◽

Author(s):

Keyword(s):

High Density ◽

High Fidelity ◽

Signal Averaging ◽

Non Invasive ◽

Invasive Measurement

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Current status of ctDNA in precision oncology for hepatocellular carcinoma

Journal of Experimental & Clinical Cancer Research ◽

10.1186/s13046-021-01940-8 ◽

2021 ◽

Vol 40 (1) ◽

Author(s):

Yan Li ◽

Yuanyuan Zheng ◽

Liwei Wu ◽

Jingjing Li ◽

Jie Ji ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Cancer Progression ◽

Diagnostic Technique ◽

Response Assessment ◽

Selection Response ◽

Circulating Tumor Dna ◽

Current Status ◽

Precision Oncology ◽

Tumor Biopsy ◽

Non Invasive

AbstractThe conventional method used to obtain a tumor biopsy for hepatocellular carcinoma (HCC) is invasive and does not evaluate dynamic cancer progression or assess tumor heterogeneity. It is thus imperative to create a novel non-invasive diagnostic technique for improvement in cancer screening, diagnosis, treatment selection, response assessment, and predicting prognosis for HCC. Circulating tumor DNA (ctDNA) is a non-invasive liquid biopsy method that reveals cancer-specific genetic and epigenetic aberrations. Owing to the development of technology in next-generation sequencing and PCR-based assays, the detection and quantification of ctDNA have greatly improved. In this publication, we provide an overview of current technologies used to detect ctDNA, the ctDNA markers utilized, and recent advances regarding the multiple clinical applications in the field of precision medicine for HCC.

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