Search for: electrophysiology
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    Biophysical implications of lipid bilayer rheometry for mechanosensitive channels

    , Article Proceedings of the National Academy of Sciences of the United States of America ; Vol. 111, Issue. 38 , 2014 , Pages 13864-13869 Bavi, N ; Nakayama, Y ; Bavi, O ; Cox, C. D ; Qin, Q. H ; Martinac, B ; Sharif University of Technology
    The lipid bilayer plays a crucial role in gating of mechanosensitive (MS) channels. Hence it is imperative to elucidate the rheological properties of lipid membranes. Herein we introduce a framework to characterize the mechanical properties of lipid bilayers by combining micropipette aspiration (MA) with theoretical modeling. Our results reveal that excised liposome patch fluorometry is superior to traditional cell-attached MA for measuring the intrinsic mechanical properties of lipid bilayers. The computational results also indicate that unlike the uniform bilayer tension estimated by Laplace's law, bilayer tension is not uniform across the membrane patch area. Instead, the highest tension... 

    Decoding Hand Trajectory from Primary Motor Cortex ECoG Using Time Delay Neural Network

    , Article Communications in Computer and Information Science ; Vol. 459 CCIS, issue , September , 2014 , p. 237-247 Kifouche, A ; Vigneron, V ; Shamsollahi, M. B ; Guessoum, A ; Sharif University of Technology
    Brain-machines - also termed neural prostheses, could potentially increase substantially the quality of life for people suffering from motor disorders or even brain palsy. In this paper we investigate the non-stationary continuous decoding problem associated to the rat's hand position. To this aim, intracortical data (also named ECoG for electrocorticogram) are processed in successive stages: spike detection, spike sorting, and intention extraction from the firing rate signal. The two important questions to answer in our experiment are (i) is it realistic to link time events from the primary motor cortex with some time-delay mapping tool and are some inputs more suitable for this mapping... 

    EEG-based functional brain networks: Hemispheric differences in males and females

    , Article Networks and Heterogeneous Media ; Volume 10, Issue 1 , March , 2015 , Pages 223-232 ; 15561801 (ISSN) Jalili, M ; Sharif University of Technology
    American Institute of Mathematical Sciences  2015
    Functional connectivity in human brain can be represented as a network using electroencephalography (EEG) signals. Network representation of EEG time series can be an efficient vehicle to understand the underlying mechanisms of brain function. Brain functional networks whose nodes are brain regions and edges correspond to functional links between them are characterized by neurobiologically meaningful graph theory metrics. This study investigates the degree to which graph theory metrics are sex dependent. To this end, EEGs from 24 healthy female subjects and 21 healthy male subjects were recorded in eyes-closed resting state conditions. The connectivity matrices were extracted using... 

    Visual acuity classification using single trial visual evoked potentials

    , Article Proceedings of the 31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Engineering the Future of Biomedicine, EMBC 2009, 2 September 2009 through 6 September 2009 ; 2009 , Pages 982-985 ; 9781424432967 (ISBN) Hajipour, S ; Shamsollahi, M. B ; Abootalebi, V ; Sharif University of Technology
    Several researches have been done to identify visual system characteristics. Some of them are based on the processing of the brain signal recordings. Visual evoked potentials (VEPs) are electrical signals which are produced in response to the visual stimuli and recorded by means of electrodes placed on the head. These signals are usually characterized by the amplitude and latency of their peaks. Different types of visual stimuli and visual system characteristics can affect the shape and hence the characteristics of VEPs. In this paper, proper visual stimuli were used and VEPs were recorded in order to classify visual acuity. To achieve this goal, visual evoked potentials were recorded and... 

    Sensorimotor control learning using a new adaptive spiking neuro-fuzzy machine, Spike-IDS and STDP

    , Article Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) ; Vol. 8681 LNCS, issue , September , 2014 , p. 379-386 Firouzi, M ; Shouraki, S. B ; Conradt, J ; Sharif University of Technology
    Human mind from system perspective deals with high dimensional complex world as an adaptive Multi-Input Multi-Output complex system. This view is theorized by reductionism theory in philosophy of mind, where the world is represented as logical combination of simpler sub-systems for human so that operate with less energy. On the other hand, Human usually uses linguistic rules to describe and manipulate his expert knowledge about the world; the way that is well modeled by Fuzzy Logic. But how such a symbolic form of knowledge can be encoded and stored in plausible neural circuitry? Based on mentioned postulates, we have proposed an adaptive Neuro-Fuzzy machine in order to model a rule-based... 

    Multivariate Synchronization Analysis of Brain Electroencephalography Signals: A Review of Two Methods

    , Article Cognitive Computation ; Volume 7, Issue 1 , February , 2013 , Pages 3-10 ; 18669956 (ISSN) Jalili, M ; Sharif University of Technology
    Springer New York LLC  2013
    Temporal synchronization of neuronal activity plays an important role in various brain functions such as binding, cognition, information processing, and computation. Patients suffering from disorders such as Alzheimer’s disease or schizophrenia show abnormality in the synchronization patterns. Electroencephalography (EEG) is a cheap, non-invasive, and easy-to-use method with fine temporal resolution. Modern multichannel EEG data are increasingly being used in brain studies. Traditional approaches for identifying synchronous activity in EEG are through univariate techniques such as power spectral density or bivariate techniques such as coherence. In this paper, we review two methods for... 

    Electricity generation from petrochemical wastewater using a membrane-less single chamber microbial fuel cell

    , Article 2012 2nd Iranian Conference on Renewable Energy and Distributed Generation, ICREDG 2012 ; 2012 , Pages 23-27 ; 9781467306652 (ISBN) Marashi, S. K. F ; Kariminia, H. R ; Sharif University of Technology
    Microbial fuel cells (MFCs) represent a new method for simultaneous wastewater treatment and biological electricity generation. In this study, petrochemical wastewater with 8000 mg/l of chemical oxygen demand was examined in a membrane-less single chamber MFC. Effects of wastewater concentration as substrate for microbial oxidation, and anode material (stainless steel or carbon brush) were investigated as designing parameters  

    One-dimensional Conduction-based Modeling of Bioenergy Production in a Microbial Fuel Cell Engaged with Multi-population Biocatalysts

    , Article Electrochimica Acta ; Volume 184 , December , 2015 , Pages 151-163 ; 00134686 (ISSN) Karimi Alavijeh, M ; Mardanpour, M. M ; Yaghmaei, S ; Sharif University of Technology
    Elsevier Ltd  2015
    Anaerobic digestion processes and the conductive electron transfer approach were used to describe the bioenergy production processes in a microbial fuel cell (MFC), respectively. The present model is a far more completed form of conduction-based modeling which is able to predict performance of an MFC fed with complex substrates and inoculated with multi-population culture. One-dimensional spatial distributions of the different microorganisms, as biocatalysts of processes and intermediates produced in the different steps of the anaerobic digestion processes in the biofilm, as well as the dynamic behavior of the anolyte including syntropic interactions among different groups of microorganisms... 

    Toward epileptic brain region detection based on magnetic nanoparticle patterning

    , Article Sensors (Switzerland) ; Volume 15, Issue 9 , September , 2015 , Pages 24409-24427 ; 14248220 (ISSN) Pedram, M. Z ; Shamloo, A ; Alasty, A ; Ghafar Zadeh, E ; Sharif University of Technology
    MDPI AG  2015
    Resection of the epilepsy foci is the best treatment for more than 15% of epileptic patients or 50% of patients who are refractory to all forms of medical treatment. Accurate mapping of the locations of epileptic neuronal networks can result in the complete resection of epileptic foci. Even though currently electroencephalography is the best technique for mapping the epileptic focus, it cannot define the boundary of epilepsy that accurately. Herein we put forward a new accurate brain mapping technique using superparamagnetic nanoparticles (SPMNs). The main hypothesis in this new approach is the creation of super-paramagnetic aggregates in the epileptic foci due to high electrical and... 

    An efficient Jacobi-like Deflationary ICA algorithm: Application to EEG denoising

    , Article IEEE Signal Processing Letters ; Volume 22, Issue 8 , December , 2015 , Pages 1198-1202 ; 10709908 (ISSN) Sardouie, S. H ; Albera, L ; Shamsollahi, M. B ; Merlet, I ; Sharif University of Technology
    Institute of Electrical and Electronics Engineers Inc  2015
    In this paper, we propose a Jacobi-like Deflationary ICA algorithm, named JDICA. More particularly, while a projection-based deflation scheme inspired by Delfosse and Loubaton's ICA technique (DelLR) is used, a Jacobi-like optimization strategy is proposed in order to maximize a fourth order cumulant-based contrast built from whitened observations. Experimental results obtained from simulated epileptic EEG data mixed with a real muscular activity and from the comparison in terms of performance and numerical complexity with the FastICA, RobustICA and DelLR algorithms, show that the proposed algorithm offers the best trade-off between performance and numerical complexity when a low number (∼... 

    Bioelectricity Generation in a Soil Microbial Fuel Cell with Biocathode Denitrification

    , Article ; Volume 37, Issue 19 , 2015 , Pages 2092-2098 ; 15567036 (ISSN) Afsham, N ; Roshandel, R ; Yaghmaei, S ; Vajihinejad, V ; Sherafatmand, M ; Sharif University of Technology
    Taylor and Francis Inc  2015
    A soil microbial fuel cell was investigated that uses soil and groundwater to generate electricity. The cathode surface area and materials are always important for increasing power. Power density was shown to be a linear function of cathode surface area. Biofilm formation on the graphite cathode was observed to be helpful in enhancing power output and maximum performance reached 89.2 mW/m2. As an application for the insertion-type soil microbial fuel cell, nitrate removing was investigated in cathode. Nitrate was reduced in an aerobic cathode at the rate of 37.5 mg nitrate/lit/day and 55 mg nitrate/lit/day in anaerobic cathode  

    Neural implant stimulation based on TiO2 nanostructured arrays; A multiphysics modeling verification

    , Article IECBES 2014, Conference Proceedings - 2014 IEEE Conference on Biomedical Engineering and Sciences: "Miri, Where Engineering in Medicine and Biology and Humanity Meet", 8 December 2014 through 10 December 2014 ; December , 2015 , Pages 677-680 ; 9781479940844 (ISBN) Sasanpour, P ; Mohammadpour, R ; Amiri, K ; Silterra; University of Malaya ; Sharif University of Technology
    Institute of Electrical and Electronics Engineers Inc  2015
    Exploiting of the nanostructure arrays as a promising candidate for excitation of neural cells has been analyzed. Based on the importance of the coupling effect between electrode and neuron, a multiphyscis modeling approach has been proposed. The model incorporates theoretically both structural effects (size, geometry) and electrophysiological effects. The system of equations for proposed model has been solved numerically using Finite Element Method for Poisson equation and Finite Difference Method for Cable equation. In this regards we have combined the system of equations in COMSOL platform with Matlab interface accordingly. We have analyzed the effect of excitation of neuron with an extra... 

    Ethylene glycol biodegradation in microbial fuel cell

    , Article Energy Sources, Part A: Recovery, Utilization and Environmental Effects ; Volume 38, Issue 8 , 2016 , Pages 1096-1102 ; 15567036 (ISSN) Hosseinpour, M ; Asadi, M ; Rahmani Eliato, T ; Vossoughi, M ; Alemzadeh, I ; Sharif University of Technology
    Taylor and Francis Inc 
    Ethylene glycol is an environmental pollutant, which exists in airport runoff and industrial waste. In this article, biodegradation of ethylene glycol in a two-chamber, batch-mode microbial fuel cell was investigated. Glucose and ethylene glycol at different concentrations were used as carbon and energy sources. Chemical oxygen demand removal in the range of 92-98% indicated that microbial fuel cell can be used for biodegradation of ethylene glycol. Ethylene glycol also improved power generation and the maximum power density was 5.72 mW/m2 (137.32 mW/m3), with respect to the same glucose and ethylene glycol concentrations (500 ppm)  

    Characteristics of early repolarization pattern in the Iranian population

    , Article Iranian Red Crescent Medical Journal ; Volume 19, Issue 3 , 2017 ; 20741804 (ISSN) Mollazadeh, R ; Sehhati, F ; Eslami, M ; Nemati, F ; Monfarednasab, M ; Sefidbakht, S ; Sharif University of Technology
    Kowsar Medical Publishing Company  2017
    Background: The early repolarization pattern (ERP) has been considered a normal variant in electrocardiography (ECG) for a long time. Nevertheless, increasing evidence has demonstrated its association with adverse outcomes. Objectives: The present study aimed to evaluate the prevalence of ERP in the Iranian general population and demonstrate its clinical and ECG correlates. Methods: A cross sectional study, comprising 1424 consecutive healthy adult individuals, was conducted at two university based hospitals in Tehran, Iran in 2012-2013. The ERP prevalence, clinical characteristics and ECG morphology were investigated in volunteers. Results: ERP was present in 136 out of 1,424 people (9.6%).... 

    Modeling of microfluidic microbial fuel cells using quantitative bacterial transport parameters

    , Article Journal of Power Sources ; Volume 342 , 2017 , Pages 1017-1031 ; 03787753 (ISSN) Mardanpour, M. M ; Yaghmaei, S ; Kalantar, M ; Sharif University of Technology
    Elsevier B.V  2017
    The objective of present study is to analyze the dynamic modeling of bioelectrochemical processes and improvement of the performance of previous models using quantitative data of bacterial transport parameters. The main deficiency of previous MFC models concerning spatial distribution of biocatalysts is an assumption of initial distribution of attached/suspended bacteria on electrode or in anolyte bulk which is the foundation for biofilm formation. In order to modify this imperfection, the quantification of chemotactic motility to understand the mechanisms of the suspended microorganisms’ distribution in anolyte and/or their attachment to anode surface to extend the biofilm is implemented... 

    Detection of human attention using EEG signals

    , Article 24th Iranian Conference on Biomedical Engineering and 2017 2nd International Iranian Conference on Biomedical Engineering, ICBME 2017, 30 November 2017 through 1 December 2017 ; 2018 ; 9781538636091 (ISBN) Alirezaei, M ; Hajipour Sardouie, S ; Sharif University of Technology
    Attention as a cognitive aspect of brain activity is one of the most popular areas of brain studies. It has significant impact on the quality of other activities such as learning process and critical activities (e.g. driving vehicles). Because of its crucial influence on the learning process, it is one of the main aspects of research in education. In this study, we propose a brand new protocol of brain signal recording in order to classify human attention in educational environments. Unlike other protocols used to record EEG signals, our protocol does not require strong memory and strong language knowledge to carry out. To this end, we have recorded EEG signals of 12 subjects using the... 

    A generalizable model for seizure prediction based on deep learning using CNN-LSTM architecture

    , Article 2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018, 26 November 2018 through 29 November 2018 ; 2019 , Pages 469-473 ; 9781728112954 (ISBN) Shahbazi, M ; Aghajan, H ; Sharif University of Technology
    Institute of Electrical and Electronics Engineers Inc  2019
    This work proposes a novel deep learning-based model for prediction of epileptic seizures using multichannel EEG signals. Multichannel images are first constructed by applying short-time Fourier transform (STFT) to Electroencephalography (EEG) signals. After a preprocessing step, a CNN-LSTM neural network is trained on the STFTs in order to capture the spectral, spatial and temporal features within and between the EEG segments and classify them as preictal or interictal stage. The proposed method achieves a sensitivity of 98.21%, a false prediction rate (FPR) of 0.13/h and a mean prediction time of 44.74 minutes on the CHB-MIT dataset. As the main contribution of this work, by using a... 

    Combined effects of electric stimulation and microgrooves in cardiac tissue-on-a-chip for drug screening

    , Article Small Methods ; Volume 4, Issue 10 , 2020 Ren, L ; Zhou, X ; Nasiri, R ; Fang, J ; Jiang, X ; Wang, C ; Qu, M ; Ling, H ; Chen, Y ; Xue, Y ; Hartel, M.C ; Tebon, P ; Zhang, S ; Kim, H.-J ; Yuan, X ; Shamloo, A ; Dokmeci, M. R ; Li, S ; Khademhosseini, A ; Ahadian, S ; Sun, W ; Sharif University of Technology
    John Wiley and Sons Inc  2020
    Animal models and traditional cell cultures are essential tools for drug development. However, these platforms can show striking discrepancies in efficacy and side effects when compared to human trials. These differences can lengthen the drug development process and even lead to drug withdrawal from the market. The establishment of preclinical drug screening platforms that have higher relevancy to physiological conditions is desirable to facilitate drug development. Here, a heart-on-a-chip platform, incorporating microgrooves and electrical pulse stimulations to recapitulate the well-aligned structure and synchronous beating of cardiomyocytes (CMs) for drug screening, is reported. Each chip... 

    Diagnosis of early Alzheimer's disease based on EEG source localization and a standardized realistic head model

    , Article IEEE Journal of Biomedical and Health Informatics ; Volume 17, Issue 6 , 2013 , Pages 1039-1045 ; 21682194 (ISSN) Aghajani, H ; Zahedi, E ; Jalili, M ; Keikhosravi, A ; Vahdat, B. V ; Sharif University of Technology
    In this paper, distributed electroencephalographic (EEG) sources in the brain have been mapped with the objective of early diagnosis of Alzheimer's disease (AD). To this end, records from a montage of a high-density EEG from 17 early AD patients and 17 matched healthy control subjects were considered. Subjects were in eyes-closed, resting-state condition. Cortical EEG sources were modeled by the standardized low-resolution brain electromagnetic tomography (sLORETA) method. Relative logarithmic power spectral density values were obtained in the four conventional frequency bands (alpha, beta, delta, and theta) and 12 cortical regions. Results show that in the left brain hemisphere, the theta... 

    Noise cancelation of epileptic interictal EEG data based on generalized eigenvalue decomposition

    , Article 2012 35th International Conference on Telecommunications and Signal Processing, TSP 2012 - Proceedings ; 2012 , Pages 591-595 ; 9781467311182 (ISBN) Hajipour, S ; Shamsollahi, M. B ; Albera, L ; Merlet, I ; Sharif University of Technology
    Denoising is an important preprocessing stage in some Electroencephalography (EEG) applications such as epileptic source localization. In this paper, we propose a new algorithm for denoising the interictal EEG data. The proposed algorithm is based on Generalized Eigenvalue Decomposition of two covariance matrices of the observations. Since one of these matrices is related to the spike durations, we should estimate the occurrence time of the spike peaks and the exact spike durations. To this end, we propose a spike detection algorithm which is based on the available spike detection methods. The comparison of the results of the proposed algorithm with the results of two well-known ICA...