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

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.

scholarly journals Fused multivariate empirical mode decomposition (MEMD) and inverse solution method for EEG source localization

2018 ◽  
Vol 63 (4) ◽  
pp. 467-479 ◽  
Author(s):  
Pegah Khosropanah ◽  
Abdul Rahman Ramli ◽  
Kheng Seang Lim ◽  
Mohammad Hamiruce Marhaban ◽  
Anvarjon Ahmedov
Keyword(s):  
Source Localization ◽  
Extraction Methods ◽  
Inverse Solution ◽  
Epileptogenic Zone ◽  
Low Resolution ◽  
Eye Blink ◽  
Multivariate Empirical Mode Decomposition ◽  
Mode Decomposition ◽  
Surgical Evaluation ◽  
Eeg Source Localization

Abstract EEG source localization is determining possible cortical sources of brain activities with scalp EEG. Generally, every step of the data processing sequence affects the accuracy of EEG source localization. In this paper, we introduce a fused multivariate empirical mode decomposing (MEMD) and inverse solution algorithm with an embedded unsupervised eye blink remover in order to localize the epileptogenic zone accurately. For this purpose, we constructed realistic forward models using MRI and boundary element method (BEM) for each patient to obtain results that are more realistic. We also developed an unsupervised algorithm utilizing a wavelet method to remove eye blink artifacts. Additionally, we applied MEMD, which is one of the recent and suitable feature extraction methods for non-linear, non-stationary, and multivariate signals such as EEG, to extract the signal of interest. We examined the localization results using the two most reliable linear distributed inverse methods in the literature: weighted minimum norm estimation (wMN) and standardized low resolution tomography (sLORETA). Results affirm the success of the proposed algorithm with the highest agreement compared to MRI reference by a specialist. Fusion of MEMD and sLORETA results in approximately zero localization error in terms of spatial difference with the validated MRI reference. High accuracy results of proposed algorithm using non-invasive and low-resolution EEG provide the potential of using this work for pre-surgical evaluation towards epileptogenic zone localization in clinics.

scholarly journals Spectrum-specific encephalography standardized low-resolution brain electromagnetic tomography network and gray matter correlations in vascular dementia patients

2020 ◽  
Vol 16 (1) ◽  
pp. 155014771989596
Author(s):  
Yan Lv ◽  
Huijuan Chen ◽  
Zhiyan Sui ◽  
Yingliu Huang ◽  
Shixiong Huang ◽  
...  
Keyword(s):  
Vascular Dementia ◽  
Topological Analysis ◽  
Cognitive Disorder ◽  
Voxel Based Morphometry ◽  
Low Resolution ◽  
Mild Dementia ◽  
Electromagnetic Tomography ◽  
Brain Electromagnetic ◽  
Dementia Patients ◽  
Tomography Analysis

Vascular dementia, secondary to Alzheimer’s dementia, ranks as one of the most frequent dementia types. The process of vascular dementia is divergent with other neurodegenerative dementias and thus reversible at the early cognitive disorder or mild dementia stages. The encephalography and neuroimaging data mining at different stages would bring neuromodulation strategies in practice; 15 mild cognitive impairment patients and 16 mild vascular dementia patients as well as 17 cognitive healthy controls were screened in this study. Cognitive tests such as Mini-Mental State Examination, Montreal cognitive assessment, voxel-based morphometry, electroencephalography, and standardized low-resolution brain electromagnetic tomography connectivity network were conducted. Compared with healthy group, voxel-based morphometry analysis showed a decrease in gray/cerebrospinal fluid ratio ( p < .05) in mild dementia group; the energy power of gamma band decreased ( p < .05) in mild dementia group; and electroencephalography standardized low-resolution brain electromagnetic tomography analysis showed wider frontal and temporal lobe involvement in mild dementia patients ( p < .05). Network topological analysis screened top 10 key Brodmann areas (44R, 7R, 8L, 22L, 47L, 27L, 1L, 1R, 7R, 43L), which could be underlying neuromodulators for dementia patients. Electroencephalography as well as structural magnetic resonance imaging could be used for the evaluation of cognitive disorder patients. The spectrum-specific standardized low-resolution brain electromagnetic tomography analysis and connectivity network analysis could shed light on the neuromodulator targets in the early phase of dementia.

EEG Mapping and Low-Resolution Brain Electromagnetic Tomography (LORETA) in Diagnosis and Therapy of Psychiatric Disorders: Evidence for a Key-Lock Principle

2005 ◽  
Vol 36 (2) ◽  
pp. 108-115 ◽  
Author(s):  
Bernd Saletu ◽  
Peter Anderer ◽  
Gerda M. Saletu-Zyhlarz ◽  
Roberto D. Pascual-Marqui
Keyword(s):  
Psychiatric Disorders ◽  
Brain Function ◽  
Senile Dementia ◽  
Normal Subjects ◽  
Low Resolution ◽  
Specific Patient ◽  
Compulsive Disorder ◽  
Electromagnetic Tomography ◽  
Brain Electromagnetic ◽  
Normal Controls

Different psychiatric disorders, such as schizophrenia with predominantly positive and negative symptomatology, major depression, generalized anxiety disorder, agoraphobia, obsessive-compulsive disorder, multi-infarct dementia, senile dementia of the Alzheimer type and alcohol dependence, show EEG maps that differ statistically both from each other and from normal controls. Representative drugs of the main psychopharmacological classes, such as sedative and non-sedative neuroleptics and antidepressants, tranquilizers, hypnotics, psychostimulants and cognition-enhancing drugs, induce significant and typical changes to normal human brain function, which in many variables are opposite to the above-mentioned differences between psychiatric patients and normal controls. Thus, by considering these differences between psychotropic drugs and placebo in normal subjects, as well as between mental disorder patients and normal controls, it may be possible to choose the optimum drug for a specific patient according to a keylock principle, since the drug should normalize the deviant brain function. This is supported by 3–dimensional low-resolution brain electromagnetic tomography (LORETA), which identifies regions within the brain that are affected by psychiatric disorders and psychopharmacological substances.

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