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Author

Munsif Ali Jatoi

Other affiliations: Indus University, Petronas
Bio: Munsif Ali Jatoi is an academic researcher from Universiti Teknologi Petronas. The author has contributed to research in topics: Electroencephalography & Inverse problem. The author has an hindex of 9, co-authored 42 publications receiving 366 citations. Previous affiliations of Munsif Ali Jatoi include Indus University & Petronas.

Papers
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Journal ArticleDOI
TL;DR: 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 and the relative advantages and limitations for each method are discussed.

144 citations

Journal ArticleDOI
TL;DR: This paper discusses and compares the ability of localizing the sources for two low resolution methods i.e., sLORETA and eLOReta respectively and corresponding activation in terms of scalp map, slice view and cortex map is discussed.
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.

102 citations

Journal ArticleDOI
TL;DR: There are negative effects of 3D movies causing significant changes in the brain activity in terms of band powers, which leads to produce symptoms of VIMS in the viewers.
Abstract: 3D movies are attracting the viewers as they can see the objects flying out of the screen. However, many viewers have reported various problems which are usually faced after watching 3D movies. These problems include visual fatigue, eye strain, headaches, dizziness, blurred vision or collectively may be termed as visually induced motion sickness (VIMS). This research focuses on the comparison between 3D passive technology with a conventional 2D technology to find that whether 3D is causing trouble in the viewers or not. For this purpose, an experiment was designed in which participants were randomly assigned to watch 2D or a 3D movie. The movie was specially designed to induce VIMS. The movie was shown for the duration of 10 min to every participant. The electroencephalogram (EEG) data was recorded throughout the session. At the end of the session, participants rated their feelings using simulator sickness questionnaire (SSQ). The SSQ data was analyzed and the ratings of 2D and 3D participants were compared statistically by using a two tailed t test. From the SSQ results, it was found that participants watching 3D movies reported significantly higher symptoms of VIMS (p value <0.05). EEG data was analyzed by using MATLAB and topographic plots are created from the data. A significant difference has been observed in the frontal-theta power which increases with the passage of time in 2D condition while decreases with time in 3D condition. Also, a decrease in beta power has been found in the temporal lobe of 3D group. Therefore, it is concluded that there are negative effects of 3D movies causing significant changes in the brain activity in terms of band powers. This condition leads to produce symptoms of VIMS in the viewers.

50 citations

Proceedings ArticleDOI
25 May 2013
TL;DR: Computational assessment of corrected EEG waveforms reveals that the proposed algorithm retrieves the EEG data by removing the eye blink artifacts reliably and compared to other eye blink artifact removal techniques, the proposed method has two benefits.
Abstract: 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.

43 citations


Cited by
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01 Jan 2016
TL;DR: As you may know, people have search numerous times for their chosen novels like this statistical parametric mapping the analysis of functional brain images, but end up in malicious downloads.
Abstract: Thank you very much for reading statistical parametric mapping the analysis of functional brain images. As you may know, people have search numerous times for their chosen novels like this statistical parametric mapping the analysis of functional brain images, but end up in malicious downloads. Rather than enjoying a good book with a cup of coffee in the afternoon, instead they cope with some infectious bugs inside their desktop computer.

1,719 citations

01 Jan 2016
TL;DR: The regularization of inverse problems is universally compatible with any devices to read and is available in the book collection an online access to it is set as public so you can download it instantly.
Abstract: Thank you for downloading regularization of inverse problems. Maybe you have knowledge that, people have search hundreds times for their favorite novels like this regularization of inverse problems, but end up in malicious downloads. Rather than reading a good book with a cup of tea in the afternoon, instead they juggled with some infectious bugs inside their computer. regularization of inverse problems is available in our book collection an online access to it is set as public so you can download it instantly. Our book servers spans in multiple locations, allowing you to get the most less latency time to download any of our books like this one. Kindly say, the regularization of inverse problems is universally compatible with any devices to read.

1,097 citations

Journal ArticleDOI
26 Feb 2019-Sensors
TL;DR: This paper tends to review the current artifact removal of various contaminations in encephalogram recordings and discusses the characteristics of EEG data and the types of different artifacts.
Abstract: Electroencephalogram (EEG) plays an important role in identifying brain activity and behavior. However, the recorded electrical activity always be contaminated with artifacts and then affect the analysis of EEG signal. Hence, it is essential to develop methods to effectively detect and extract the clean EEG data during encephalogram recordings. Several methods have been proposed to remove artifacts, but the research on artifact removal continues to be an open problem. This paper tends to review the current artifact removal of various contaminations. We first discuss the characteristics of EEG data and the types of different artifacts. Then, a general overview of the state-of-the-art methods and their detail analysis are presented. Lastly, a comparative analysis is provided for choosing a suitable methods according to particular application.

398 citations

Journal ArticleDOI
TL;DR: A reorganized the causes of VR sickness into three major factors (hardware, content, and human factors) and investigated the sub-component of each factor and proposed a multimodal fidelity hypothesis to give an insight into future studies.
Abstract: In virtual reality (VR), users can experience symptoms of motion sickness, which is referred to as VR sickness or cybersickness. The symptoms include but are not limited to eye fatigue, disorientat...

275 citations

Journal ArticleDOI
TL;DR: This paper covers some of the state-of-the-art seizure detection and prediction algorithms and provides comparison between these algorithms and concludes with future research directions and open problems in this topic.
Abstract: Epilepsy patients experience challenges in daily life due to precautions they have to take in order to cope with this condition. When a seizure occurs, it might cause injuries or endanger the life of the patients or others, especially when they are using heavy machinery, e.g., deriving cars. Studies of epilepsy often rely on electroencephalogram (EEG) signals in order to analyze the behavior of the brain during seizures. Locating the seizure period in EEG recordings manually is difficult and time consuming; one often needs to skim through tens or even hundreds of hours of EEG recordings. Therefore, automatic detection of such an activity is of great importance. Another potential usage of EEG signal analysis is in the prediction of epileptic activities before they occur, as this will enable the patients (and caregivers) to take appropriate precautions. In this paper, we first present an overview of seizure detection and prediction problem and provide insights on the challenges in this area. Second, we cover some of the state-of-the-art seizure detection and prediction algorithms and provide comparison between these algorithms. Finally, we conclude with future research directions and open problems in this topic.

215 citations