Munsif Ali Jatoi

A survey of methods used for source localization using EEG signals

Abstract The EEG source localization which is used to localize the electrical activity of brain has been an active area of research as it provides useful information for study of brains physiological, mental and functional abnormalities. This problem is called EEG inverse problem. The localization of the active sources needs the solution of ill posed EEG inverse problem. Since the foundation of this field till today, many methods have been developed with the aim of in-depth localization, high resolution, reduction in localization/energy error and decreased computational time. In this survey, EEG inverse problem is discussed with its primary to most developed and recent solutions. The introduction to the field along with the categorization of different solutions is provided. Also, the relative advantages and limitations for each method are discussed. Finally, the challenges and future recommendations are provided, in the end, for further improvement of EEG inverse problem in terms of resolution, computational power and localization error.

Automatic eye-blink artifact removal method based on EMD-CCA

This research proposes a new hybrid algorithm for automatic removal of eye blink artifact from EEG data based on empirical mode decomposition (EMD) and canonical correlation analysis (CCA). The validity and efficiency of the proposed algorithm is evaluated using correlation coefficient and signal-to-artifact ratio (SAR) and the proposed algorithm is also compared with other popular eye blink artifact removal techniques (CCA, ICA, EMD-ICA) on simulated EEG data of two channels. From the simulation results, the average correlation coefficients for the EEG channels are obtained as 0.908 and 0.864 respectively. The SAR of the EEG signal also improved from 2.2 dB to 6.0 dB after correction using our proposed method. Compared to other eye blink artifact removal techniques, our proposed method has two benefits. Firstly, no visual inspection is required to detect the eye blink artifact components. Secondly, computational assessment of corrected EEG waveforms reveals that the proposed algorithm retrieves the EEG data by removing the eye blink artifacts reliably.

Epileptic seizure detection using the singular values of EEG signals

A new technique based on Singular Value Decomposition (SVD) for the detection of epileptic seizures is proposed. The SVD is applied sequentially on a sliding window of one second width of EEG data and the r singular values are obtained and used to indicate sudden changes in the signals. EEG recordings of 4-paediatric patients with 20 seizures are used to validate the proposed algorithm and the preliminary results indicates good level of sensitivity by the singular values to the changes in the EEG signals due to epileptic seizure. This sensitivity can be used to develop more reliable seizure detector than the existing techniques.

EEG based brain source localization comparison of sLORETA and eLORETA.

Abstract Human brain generates electromagnetic signals during certain activation inside the brain. The localization of the active sources which are responsible for such activation is termed as brain source localization. This process of source estimation with the help of EEG which is also known as EEG inverse problem is helpful to understand physiological, pathological, mental, functional abnormalities and cognitive behaviour of the brain. This understanding leads for the specification for diagnoses of various brain disorders such as epilepsy and tumour. Different approaches are devised to exactly localize the active sources with minimum localization error, less complexity and more validation which include minimum norm, low resolution brain electromagnetic tomography (LORETA), standardized LORETA, exact LORETA, Multiple Signal classifier, focal under determined system solution etc. This paper discusses and compares the ability of localizing the sources for two low resolution methods i.e., sLORETA and eLORETA respectively. The ERP data with visual stimulus is used for comparison at four different time instants for both methods (sLORETA and eLORETA) and then corresponding activation in terms of scalp map, slice view and cortex map is discussed.

EEG-based brain source localization using visual stimuli

Electroencephalography (EEG) is widely used in variety of research and clinical applications which includes the localization of active brain sources. Brain source localization provides useful information to understand the brains behavior and cognitive analysis. Various source localization algorithms have been developed to determine the exact locations of the active brain sources due to which electromagnetic activity is generated in brain. These algorithms are based on digital filtering, 3D imaging, array signal processing and Bayesian approaches. According to the spatial resolution provided, the algorithms are categorized as either low resolution methods or high resolution methods. In this research study, EEG data is collected by providing visual stimulus to healthy subjects. FDM is used for head modelling to solve forward problem. The low‐resolution brain electromagnetic tomography (LORETA) and standardized LORETA (sLORETA) have been used as inverse modelling methods to localize the active regions in the brain during the stimulus provided. The results are produced in the form of MRI images. The tables are also provided to describe the intensity levels for estimated current level for the inverse methods used. The higher current value or intensity level shows the higher electromagnetic activity for a particular source at certain time instant. Thus, the results obtained demonstrate that standardized method which is based on second order Laplacian (sLORETA) in conjunction with finite difference method (FDM) as head modelling technique outperforms other methods in terms of source estimation as it has higher current level and thus, current density (J) for an area as compared to others.

Representing EEG source localization using Finite Element Method

Finite Element Method (FEM) is a numerical tool usually used to solve various problems related to electromagnetic field, biomechanics, stress analysis etc. In this paper, the finite element is proposed as a solution to the localization problem of the active sources inside the brain. This localization is termed as the EEG Inverse problem. The solution to EEG inverse problem with less localization error, high resolution and less computational complexity leads to better understanding of human brain behavior and helps neurologist and neurosurgeons in curing various neurological disorders. The implementation of the FEM in solving EEG inverse problem is explained and then a pseudo code in MATLAB is designed and explained for the application to solve the problem. However, for illustration purpose, the solution to the 1D electromagnetic problem through FEM is plotted to elaborate graphically the procedure.

BEM based solution of forward problem for brain source estimation

The localization of active sources inside the brain is termed as brain source localization. However, when the neuroimaging technique is EEG, then it is specifically termed as EEG source localization. This problem is also referred to as EEG inverse problem. The source localization problem is defined by forward problem and inverse problem. For the forward problem, head modelling is carried out by using either analytical methods or by using numerical techniques such as finite element method (FEM), boundary element method (BEM) and finite difference method (FDM). This head modelling information is further used to localize the active regions by estimating the current density by using various inverse algorithms. This research discusses the usage of boundary element method (BEM) for the modelling of head and consequently generation of simulated data. The results have shown that by simulating dipole on the cortical surface, the simulated EEG data can be generated. Hence, after the generation of simulated data, the inverse techniques are applied for the localization of active sources. This information can be used for the estimation of active sources inside the brain during various physical activities and for localizing of brain parts for the diagnoses of various brain disorders.

Low Resolution Brain Source Localization Using EEG Signals

Each mental or physical task gives rise to generate electromagnetic activity in the brain. These electrical signals are analyzed by using various neuroimaging techniques which include electroencephalography (EEG), magnetoencephalogy (MEG), positron emission tomography (PET) and functional magnetic resonance imaging (fMRI). However, when the brain sources which are responsible for such electrical activity are localized, then it’s called brain source localization or source estimation. This information is utilized to comprehend brain’s physiological, pathological, mental, functional abnormalities. Also, the information is used to diagnose cognitive behaviour of the brain. Various methodologies based upon EEG signals are adopted to localize the active sources such as minimum norm estimation (MNE), low resolution brain electromagnetic tomography (LORETA), standardized LORETA, exact LORETA, multiple signal classification (MUSIC), focal underdetermined system solution (FOCUSS) etc. This research discusses localizing ability of low resolution techniques (LORETA and sLORETA) for various head models (finite difference model and concentric model). The simulations are carried out by using NETSTATION software. The results are compared in terms of activations for same EEG data with the same stimulus provided to subjects. However, it is observed that the combination of finite difference method (FDM) with sLORETA produced best results in terms of source intensity level (nA). Hence, the combination of inverse method sLORETA with FDM produces better source localization.

Bayesian Algorithm Based Localization of EEG Recorded Electromagnetic Brain Activity

BACKGROUND Electrical signals are generated inside human brain due to any mental or physical task. This causes activation of several sources inside brain which are localized using various optimization algorithms. METHODS Such activity is recorded through various neuroimaging techniques like fMRI, EEG, MEG etc. EEG signals based localization is termed as EEG source localization. The source localization problem is defined by two complementary problems; the forward problem and the inverse problem. The forward problem involves the modeling how the electromagnetic sources cause measurement in sensor space, while the inverse problem refers to the estimation of the sources (causes) from observed data (consequences). Usually, this inverse problem is ill-posed. In other words, there are many solutions to the inverse problem that explains the same data. This ill-posed problem can be finessed by using prior information within a Bayesian framework. This research work discusses source reconstruction for EEG data using a Bayesian framework. In particular, MSP, LORETA and MNE are compared. RESULTS The results are compared in terms of variational free energy approximation to model evidence and in terms of variance accounted for in the sensor space. The results are taken for real time EEG data and synthetically generated EEG data at an SNR level of 10dB. CONCLUSION In brief, it was seen that MSP has the highest evidence and lowest localization error when compared to classical models. Furthermore, the plausibility and consistency of the source reconstruction speaks to the ability of MSP technique to localize active brain sources.

Framework for estimating active brain sources using MUSIC and Root MUSIC

Subspace techniques are widely used for direction of arrival (DOA) problems in telecommunications and position location applications for estimating the location of sources from where the signal is originated. This source estimation problem is analogues to the source estimation problem in EEG signal processing commonly termed as EEG inverse problem. The EEG inverse problem goes for estimation of active source inside the brain which is responsible for overall electromagnetic activity. This estimation provides useful basis to understand the physiological, neural and cognitive behavior of human brain which ultimately can be used for cure of many CNS related disease such as epilepsy and tumour etc. This research discuss most commonly used subspace techniques such as Multiple Signal classifier (MUSIC) and Root MUSIC for general DOA problem and produces some results by using MATLAB for arbitrary number of sources and varied number of element. Thus, the same methodology can be adopted for localization of active brain sources with few exceptions such as forward head modeling and sensor positions.