Biomedical Signal Processing / Medical Signal Processing / Biosignal Processing
Ali Khadem; Gholam Ali Hossein-Zadeh
Volume 8, Issue 1 , March 2014, , Pages 1-17
Abstract
In EEG/MEG datasets, the Volume Conduction (VC) artifact appears as instantaneous linear mixing of brain source activities on the channel measurements. A desired characteristic of an ideal EEG/MEG connectivity estimator (on sensor-space) is its robustness to VC artifact. This means that the VC of independent ...
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In EEG/MEG datasets, the Volume Conduction (VC) artifact appears as instantaneous linear mixing of brain source activities on the channel measurements. A desired characteristic of an ideal EEG/MEG connectivity estimator (on sensor-space) is its robustness to VC artifact. This means that the VC of independent brain sources must never lead to detection of significant connectivity among EEG/MEG channels. There has been no criterion in the literature so far that can compare the robustness levels of different (sensor-space) connectivity estimators against VC artifact. In this paper, a criterion called Robustness Index (RI) is proposed to compare the robustness levels of connectivity estimators to channel couplings which are modeled by instantaneous linear mixing of quasi-independent components. Since the VC effects have instantaneous linear mixing nature, we expect RI to rank the connectivity estimators according to their robustness levels to VC artifact. RI is used to rank seven functional connectivity estimators: the absolute value of Pearson Correlation Coefficient (CC), Mutual Information (MI), Magnitude Squared Coherence (Coh), (1:1) Phase Locking Value ((1:1)PLV), the absolute value of Imaginary part of Coherency (ImC), Phase Lag Index (PLI) and Weighted Phase Lag Index (WPLI). The results for simulated data and a real EEG dataset show the connectivity estimators that are theoretically robust to VC artifact (ImC, PLI and WPLI) yield RI values near %100 and have the highest ranks, as expected. Also, for the simulated models in which the true VC effects and brain sources are known, ranking the connectivity estimators by RI is consistent with their robustness levels against VC artifact. This supports the possibility of using RI as a tool for ranking the robustness levels of connectivity estimators against VC artifact for real EEG/MEG datasets.
Tissue Engineering
Zahra Saghaei Noosh Abadi; Atefe Aghajani; Mohammad Haghpanahi
Volume 7, Issue 1 , June 2013, , Pages 1-11
Abstract
We introduce how we may produce an experimental phantom for modeling the mechanical properties of soft tissue. Gelatin materials are used to construct the phantom. Our phantom comprises of two different types of tissue; tumor and background normal tissue. Weight ratio of the dry gelatin and deionized ...
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We introduce how we may produce an experimental phantom for modeling the mechanical properties of soft tissue. Gelatin materials are used to construct the phantom. Our phantom comprises of two different types of tissue; tumor and background normal tissue. Weight ratio of the dry gelatin and deionized water are obtained for producing the young’s modulus of 21 kPa and 102 kPa for the normal tissue and tumor, respectively. This phantom is used in ultrasound elastography with external excitation less than 5%.
Biomechanics of Bone / Bone Biomechanics
Masume Khaghani; Zahra Golniya; Ali Doostmohammadi
Volume 6, Issue 1 , June 2012, , Pages 1-7
Abstract
The aim of this work was evaluating of zirconia nanoparticles’ effect on physical and mechanical properties of dental glass ionomer cements (GICs). Ceramic part of GIC was prepared using melting method and zirconia nanoparticles were added to GIC in 1, 3 and 5 weight percent. Characterization tests ...
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The aim of this work was evaluating of zirconia nanoparticles’ effect on physical and mechanical properties of dental glass ionomer cements (GICs). Ceramic part of GIC was prepared using melting method and zirconia nanoparticles were added to GIC in 1, 3 and 5 weight percent. Characterization tests and compressive strength evaluation of nanocomposite samples were carried out. The XRD results showed that the prepared ceramic part of GIC was completely amorphous and can be used as the matrix of composite. The result of XRF showed that the chemical composition of ceramic part of GIC was similar to expected composition. Also the results of mechanical properties determination analysis showed that the addition of zirconia nanoparticles to GIC could improve the compressive strength. The maximum of this strength obtained using 1% wt of GIC in zirconia composite. Increasing the nanoparticles content resulted in decrease of compressive strength but the strength of composite with any composition was more than the strength of control sample. According to the results of this study, the most proper composite was the one containing1%wt zirconia nanoparticles.
Fluid-Structure Interaction in Biological Media / FSI
Alireza Hashemi Fard; Nasser Fatouraee
Volume 5, Issue 1 , June 2011, , Pages 1-12
Abstract
The heart muscle is supplied via the coronary arteries. The coronary arteries are deformed in each cardiac cycle by the contraction of the myocardium. The aim of this work was to investigate the effects of physiologically idealized cardiac-induced motion on flow rate in human left coronary arteries. ...
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The heart muscle is supplied via the coronary arteries. The coronary arteries are deformed in each cardiac cycle by the contraction of the myocardium. The aim of this work was to investigate the effects of physiologically idealized cardiac-induced motion on flow rate in human left coronary arteries. The blood flow rate were numerically simulated in an elastic modeled left anterior descending coronary artery (LAD) having a uniform circular cross section. Blood was considered to be a non-Newtonian fluid and Arterial motion was specified based on monoplane physiologically idealized bending. Simulations were carried out with dynamic pressure difference conditions between inlet and outlet in both fixed and moving LAD models, to evaluate the relative importance of LAD motion, flow rate, and the interaction between motion and time-averaged flow rate. LAD motion was caused variations in time-averaged flow rate in the moving LAD models as compare as the fixed models. There was significant variability in the magnitude of this motion-induced flow variation. However, the magnification of time-averaged flow rate is depending to specification of the cardiac motion. Furthermore, the effects of pressure pulsatility dominated LAD motion induced effects; specifically, there were local flow variation and secondary flow in the simulations conducted in moving LAD models.
Biomedical Image Processing / Medical Image Processing
Effat Yahaghi; Yashar Nohi; Amir Movafeghi; Hamid Soltanian Zadeh
Volume 4, Issue 1 , June 2010, , Pages 1-11
Abstract
Magnetic resonance imaging (MRI) is a non-ionizing method for identification and evaluation of soft tissue lesions. Perfusion MRI evaluates soft tissues by measuring changes in magnetization of water molecules due to a contrast agent. To this end, concentration curves in the plasma and tissue are estimated ...
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Magnetic resonance imaging (MRI) is a non-ionizing method for identification and evaluation of soft tissue lesions. Perfusion MRI evaluates soft tissues by measuring changes in magnetization of water molecules due to a contrast agent. To this end, concentration curves in the plasma and tissue are estimated by MRI and effective longitudinal relaxation time (T1eff) of the tissue was calculated. To interpret the results, the effects of water exchange on the effective longitudinal relaxation time should be studied. This work presents such a study in which the equations of two- and three-compartmental models of rat brain tissue are solved using Hion and Runge-Kutta numerical methods for different input functions and simulated by Monte Carlo method. Since the exchange of water and contrast agent among different tissue compartments is a diffusion phenomenon, Monte Carlo method is applicable. Results of the numerical methods were compared with those of Monte Carlo simulation. The results of the two methods were almost identical with a maximum relative difference of less than 1%. In this work, concentration of contrast agent in plasma is estimated from MRI of a rat brain tissue. This data is used in the Monte Carlo method to obtain T1eff and exchange rate constants. An advantage of our method is that T1eff is obtained from real data and not from the curve fitting method as commonly used. We derive concentration of contrast agent as a function of time in extravascular space for different constants (K). Then, the curves of simulated and real data were compared to obtain the exchange rate constant of each compartment. The results showed that K of an abnormal tissue was larger than that of the normal tissues. As such, this parameter may be used for diagnosis and treatment of the soft tissue diseases.
Biomedical Image Processing / Medical Image Processing
Hossein Rabbani
Volume 3, Issue 1 , June 2009, , Pages 1-14
Abstract
In this paper, ultrasonic images are initially deblurred using Gradient method and then the estimations of image and point spread function (PSF) are improved using denoising techniques. For this reason, at first a criterion with appropriate regularizers (that results in preservation of the edges) is ...
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In this paper, ultrasonic images are initially deblurred using Gradient method and then the estimations of image and point spread function (PSF) are improved using denoising techniques. For this reason, at first a criterion with appropriate regularizers (that results in preservation of the edges) is defined for the iterative Gradient method, then the estimation of PSF is improved using a denoising technique based on using an anisotropic window around each pixel. The initial estimation of image is also improved using a denoising method in complex wavelet domain that proposes maximum a posteriori (MAP) estimator and local Laplacian prior density function. Using these denoising methods on top of Gradient method causes that our algorithm reduces the visual artifacts and preserves the edges in the deblurred images. Our simulations show that the proposed method in this paper outperforms other methods visually and quantitatively.
Fluid-Structure Interaction in Biological Media / FSI
Hanie Niroomand Oscuii; Farzan Ghalichi; Mohammad Tafazzoli Shadpour
Volume 2, Issue 1 , June 2008, , Pages 1-8
Abstract
In this paper, we studied the effect of mechanical loading on remodeling process with aging in muscular arteries. Based on the gathered experimental data, the brachial artery was selected for simulation. In this simulation, pulsatile pressure and flow waves were considered as boundary conditions to study ...
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In this paper, we studied the effect of mechanical loading on remodeling process with aging in muscular arteries. Based on the gathered experimental data, the brachial artery was selected for simulation. In this simulation, pulsatile pressure and flow waves were considered as boundary conditions to study the effect of circumferential stress and wall shear stress on the remodeling process. FSI based transient numerical simulation was used to solve the fluid and solid equations. The results of three remodeling schemes showed that inward eutrophic scheme is an optimum algorithm for brachia! Artery remodeling with aging. Such remodeling scheme causes the most optimized outcome to keep circumferential stress with minimal alteration.
Bioheat Transfer
Farzan Ghalichi; Sohrab Behnia
Volume 1, Issue 1 , June 2007, , Pages 1-8
Abstract
The methods of focusing ultrasonic waves in order to apply hyperthermia cancer therapy have studied and a transducer capable of focusing waves on cancerous tissues with the aid of its piezoelectricelements has introduced. The amount of absorbed energy was computed by solving numerically the acoustic ...
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The methods of focusing ultrasonic waves in order to apply hyperthermia cancer therapy have studied and a transducer capable of focusing waves on cancerous tissues with the aid of its piezoelectricelements has introduced. The amount of absorbed energy was computed by solving numerically the acoustic pressure equation using Rayleigh-Summerfield Integral, with the intention to determine the optimum spatial array of piezoelectric elements for energy concentration. In order to control the treatment procedure, the numerical solution of Bio-heat Transfer Equation (BHTE), along with the finite-element simulation of thermal energy distribution in a cervix cancerous tissue is considered.
Biomechanics of Bone / Bone Biomechanics
Khalil Farhangdoust; Ali Banihashem; Ali Ghaneei
Volume -2, Issue 1 , July 2005, , Pages 1-8
Abstract
Using ceramic coatings has increased in popularity due to their compatibility with bone, absence of the fibrous layer at the coating-implant interface, and the stronger coating-bone bonding. Among these coatings, hydroxyapatite (HA) and fluoroapatite (FA) are more popular. For the first time in this ...
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Using ceramic coatings has increased in popularity due to their compatibility with bone, absence of the fibrous layer at the coating-implant interface, and the stronger coating-bone bonding. Among these coatings, hydroxyapatite (HA) and fluoroapatite (FA) are more popular. For the first time in this paper, modeling and stress analysis have been carried out for 24 implants in an axisymetric form using the finite element technique. Twelve of these samples belong to IMZ and the rest are from Dyna system. All implants had HA and FA coatings with thicknesses between 10 to 100 microns. The stress analysis results show that the stress concentration at the implant-coating and bone-coating bonding surfaces decreases with the increase of coating thickness. In addition, stress concentrations for implants with FA coatings are always more than those with HA coatings. In all implants, stress concentration has been observed around the bone crest.
Spinal Biomechanics
Mojtaba Shahab; Behzad Seyfi; Nasser Fatouraee; Amir Saeid Seddighi
Volume 9, Issue 1 , April 2015, , Pages 1-15
Abstract
Spinal deformities are generally associated with lumbar and cervical chronic pain and additionally they disturb the health. In these deformities, lumbar spinal curvature undergone changes in three dimensional space and in most cases, they cause reduction of lung capacities, breathing problems and negative ...
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Spinal deformities are generally associated with lumbar and cervical chronic pain and additionally they disturb the health. In these deformities, lumbar spinal curvature undergone changes in three dimensional space and in most cases, they cause reduction of lung capacities, breathing problems and negative effects on cardiovascular system. In critical deformity cases, in order to correct the deformity and prevent its progression, surgeons determine to perform posterior spinal fusion. As a result, they need to extract some important clinical parameters of spine such as Cobb angle, sagittal and coronal balance, spinal curvature, vertebraes angles and their rotations. In this study, edited tomographic images in MIMICS, were used to prepare a three dimensional model of the spine. Then by using curve fitting techniques and different clustering methods such as self-organization nueral network, k-means and hierarchical method, vertebras were separated and important geometrical data such as curvature of the spine and vertebras angle were obtained. In addition, through implementation of certain algorithms, other clinical features of each vertebra, including minimum and maximum height, length and width of the vertebral body and the relative displacement of vertebras were calculated automatically. In order to validate the proposed methods, measures and angles; derived values obtained automatically at each stage, were again calculated by a radiologist and a spine surgeon who was unaware of the goals of the research. Automatic values were verified by being compared with these manual results. In conclusion the reliability, accuracy and performance of the proposed automatic algorithms were demonstrated.
Biomedical Signal Processing / Medical Signal Processing / Biosignal Processing
Maryam Tavakoli Najafabadi; Vahid Abootalebi; Farzaneh Shayegh
Volume 10, Issue 1 , May 2016, , Pages 1-10
Abstract
The purpose of this article is to evaluate the efficiency of Canonical Correlation Analysis- Recursive Least Square (CCA-RLS)hybridmethod in ElectroOcluGram (EOG) artifact removal from ElectroEncephaloGram (EEG) signal and compare it with Independent Component Analysis (ICA), Canonical Correlation Analysis ...
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The purpose of this article is to evaluate the efficiency of Canonical Correlation Analysis- Recursive Least Square (CCA-RLS)hybridmethod in ElectroOcluGram (EOG) artifact removal from ElectroEncephaloGram (EEG) signal and compare it with Independent Component Analysis (ICA), Canonical Correlation Analysis (CCA), Recursive Least Square (RLS)methods and ICA-RLS hybrid method. After decomposition of the noisy signal by CCA, the noisy components aredetected based ontheir kurtosis, and are filtered by RLS. As the result,the enhanced signal is reconstructed by mixing the original noise-free components and filtered components. In order to compare the methods quantitatively, two evaluation criteria, namely Mean Square Error (MSE) and Signal to Noise Ratio (SNR) are used.The MSE and SNR average values were calculated for five subject in four different channels. EEG data are taken from BCI2008. According to the results,the combination of CCA-RLS method has better performance compareto the other methods used in this paper.
Biological Computer Modeling / Biological Computer Simulation
Seyed Hojat Sabzpoushan; Fateme Pourhasanzade
Volume 11, Issue 1 , May 2017, , Pages 1-18
Abstract
In this paper, a new method is proposed for slowing down avascular tumor growth. Our method is established on an agent based avascular tumor growth model (ABM). The model is based on biological assumptions with regard to the immune system interactions. The model parameters are fitted in compatability ...
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In this paper, a new method is proposed for slowing down avascular tumor growth. Our method is established on an agent based avascular tumor growth model (ABM). The model is based on biological assumptions with regard to the immune system interactions. The model parameters are fitted in compatability with cancer biology using in vivo expremental data. The immune cells recruitment, which usually occur after that tumor cells are identified, are also considered in ABM model. The results show that the proposed model not only is able to simulate the tumor growth graphically, but also the in vivo tumor growth quantitatively and qualitatively. Besides, the model proposes a new idea for slowing down the tumor growth considering two types of prolaiferative tumor cells, i.e. the tumor will grow slowly if the division probability of the proliferative tumor cells depends on the microenvironmental conditions. The proposed idea has been validated using an in silico simulation.
Neuro-Muscular Engineering
Tahmineh Sadati; Mohammad Reza Daliri
Volume 12, Issue 1 , June 2018, , Pages 1-10
Abstract
A brain-computer interface is a system which works based on the neural activity created by the brain and it has attracted the attention of many researchers in recent years. These interfaces are independent of the usual pathway of peripheral and muscular nerves and are very important because of their ...
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A brain-computer interface is a system which works based on the neural activity created by the brain and it has attracted the attention of many researchers in recent years. These interfaces are independent of the usual pathway of peripheral and muscular nerves and are very important because of their ability to provide a new dimension in communication or control of a device for the disabled persons. The neural activity used in the brain-computer interface can be recorded by various invasive and non-invasive methods and can be converted to the desired signal by different decoding algorithms. In this study, 3 rats were used to perform a movement task which was pressing a key and receiving a drop of water by a mechanical arm for corrected trials. By implanting a 16-channel microelectrode array in the rat's motor cortex during an invasive process, the brain signals are recorded during the task, and simultaneously the signal received by the force sensor is also stored. By performing the necessary preprocessing on spikes and extracting the firing rates of signal as a feature vector by convolving a Gaussian window with the spike trains, the necessary inputs for the decoding algorithm, which is linear regression here, are obtained. Two patterns have been used for cross validation. The first pattern considers 60% of the data from the beginning of the signal as a train set and the remaining 40% of the signal as a test set and the second pattern is the opposite of the first one. Several methods have been used to evaluate the decoding algorithm used in the studies. In this paper, the correlation coefficient and coefficient of determination have been used. The correlation coefficient and coefficient of determination between the desired force and predicted force in linear resgression method, in average of three sessions for three rats, are equal to r=0.56 and =0.20 for the first pattern and r=0.55 and =0.30 for the second pattern respectively. These results show that firing rates of neurons are proper features to predict continous variables such as force. Besides, it can be concluded that linear regression is a suitable method for decoding a motor variable like force and follows the desired signal properly.
Biofluid Mechanics / Biofluids
Mohammad Ahmadi Alashti; Bahman Vahidi; Mahtab Ebad
Volume 13, Issue 1 , April 2019, , Pages 1-15
Abstract
The large surface area of the lung with its thin air-blood barrier is exposed to particles in the inhaled air. In this condition, if the inhaled pollutant aerosols are toxic, the particle-lung interaction may cause serious hazards and injuries on human’s health. On the otherhand, these interactions ...
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The large surface area of the lung with its thin air-blood barrier is exposed to particles in the inhaled air. In this condition, if the inhaled pollutant aerosols are toxic, the particle-lung interaction may cause serious hazards and injuries on human’s health. On the otherhand, these interactions are also used for drug delivery to human’s body. In either case, an accurate estimation of dose and sites of deposition in the respiratory tract is fundamental for understanding mechanobiology of these deseases. Obtaining in vivo data of particle transportation in the human lung experimentally is often difficult. But, computational fluid-particle dynamics (CFPD) has provided the possibility to gain aerosol transportion data in realistic airway geometries. Aerosols deposition in the human lung mainly occurs due to combination of inertial impaction, gravitational sedimentation and diffusion. For particles with aerodynamic size of 0.5 to 5 micron and in inhalation state of lung, the main mechanisms of particle deposition in distal parts of human’s respiratory system are sedimentation, due to gravity and convective transfer due to wall movement. In this study, deposition of particles in distal part of human respiratory system, specifically 18th generation, has been modeled for two gravity conditions, normal and absent gravity, by assuming isotropic displacements on the walls and with the rate of 1 (mg/sec) for particle input. By analyzing the results, it was determined that the amount of particle deposition in distal airways reduces a great amount by omitting the effect of gravitational force because, particles smaller than 5 micron can penetrate into that airways. Particles with the diameter of 5 micron deposit under the effect of inertial impact, whereas this mechanism occurs mostly in airways with large and medium diameters and also, by sedimentation which occurs in the distal lung.
Fatemeh Ghafouri; Mohammad Hadi Honarvar; Mohammad Mahdi Jalili
Volume 14, Issue 1 , May 2020, , Pages 1-11
Abstract
Minimizing the energy expenditure as well as structure's size and weight is very important in biped walking robots. To achieve this target, a passive controller, which is a combination of spring and linear damper, is added to a biped walker. The important specification of the studied walker is that it ...
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Minimizing the energy expenditure as well as structure's size and weight is very important in biped walking robots. To achieve this target, a passive controller, which is a combination of spring and linear damper, is added to a biped walker. The important specification of the studied walker is that it has two convex soles at the end of the legs as feet, which is jointed to body with a passive revolute joint. Contact point moves on a sole curve. To reduce system's dynamic complexity, pointed mass approach is used. The main purpose of this research is studying the dynamical behavior of this underactuated walker before and after adding controller. In the first step, a model based on developed pointed mass model is offered and analyzed by adding two rigid convex soles as feet and passive revolute joint as ankle. To make leg length changes during walking, an active dynamic element is used. Next, a passive controller or dynamic element is used with the active one to reduce active element role during movement. Particle swarm optimization method is used to minimize this role by calculating optimized passive element parameters. The results show using the combination of optimized passive and active dynamic elements, the amount of energy consumption is decreased significantly. As a result, we can use a much smaller active element with less power to walk. Also using a passive dynamic element practically improves mechanical specifications of the structure such as dimensions and weight as well as providing simple use for users.
Gait Analysis
Fatemeh Akbarifar; Mohammad Hadi Honarvar; Mostafa Haj Lotfalian
Volume 15, Issue 1 , May 2021, , Pages 1-11
Abstract
Finding the center of rotation (COR) is needed for defining the anatomical axis of the skeletal system and for the kinematic calculation of joints in biomechanical studies. For this purpose, predictive and functional methods can be used. In the predictive methods, regression equations obtained from anthropometric ...
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Finding the center of rotation (COR) is needed for defining the anatomical axis of the skeletal system and for the kinematic calculation of joints in biomechanical studies. For this purpose, predictive and functional methods can be used. In the predictive methods, regression equations obtained from anthropometric measurements are used, and in the functional methods, the relative motion of the two adjacent segments is used to find COR. The purpose of this study is to formulate the circle fitting algorithm as a functional method with two analytical and optimization solutions. In order to evaluate the algorithm, error analysis was performed by both analytical and numerical methods. Also, effective factors in error estimating of COR position such as standard deviation of measurement system error (σ), rotation angle (α) and the distance between marker and COR (r), was evaluated. The results showed a high correlation (r=0.99) between analytical and numerical solution, which proved the accuracy of the error analysis. In this study, optimization method according to the accuracy of better estimates in low quantities α, less influence on high quantities σ and high speed in problem solving, can be taken into consideration to reconstruct human movements in biomechanical studies. Use of functional methods, eliminates the need for attaching markers to anatomical landmarks and provides a new development in motion data acquisition.
Brain Computer Interface / BCI / Neural Control Int. / NCI / Mind Machine Int. / MMI / Direct Neural Int. / DNI / Brain Machine Int. / BMI
Marzie Alirezaei Alavijeh; Ali Maleki
Volume 16, Issue 1 , May 2022, , Pages 1-9
Abstract
Nowadays, brain-computer interface system based on steady-state visual evoked potentials is increased due to advantages such as accepted accuracy and minimal need for user training. Despite these benefits, the unwanted noise that affects SSVEP is one of the issues that can reduce the efficiency of such ...
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Nowadays, brain-computer interface system based on steady-state visual evoked potentials is increased due to advantages such as accepted accuracy and minimal need for user training. Despite these benefits, the unwanted noise that affects SSVEP is one of the issues that can reduce the efficiency of such systems. This paper uses the EMD algorithm in the initial phase and CCA or LASSO for the recognition of the stimulation frequency. In the first step, the EMD algorithm is applied so that non-stationary SSVEP signal breaks into oscillating functions and meaningful information are extracted. Among the IMFs obtained from the EMD method, only IMFs whose amplitude of the frequency spectrum in the frequency ranges corresponding to the excitation is higher were selected. With this selection, noisy signals and unprofitable information can be omitted. In the proposed method, two CCA and LASSO diagnostic methods were performed on the sum of selected signals to identify the frequency of stimulation. The simulation results show the recognition accuracy of 81.76% and 82.26% for the proposed method EMD-CCA and EMD-LASSO, respectively. While detection accuracy is 78.10% and 78.72% for conventional methods of CCA and LASSO.
Brain Computer Interface / BCI / Neural Control Int. / NCI / Mind Machine Int. / MMI / Direct Neural Int. / DNI / Brain Machine Int. / BMI
maryam farhadnia; Sepideh Hajipour; mohammad mikaili
Volume 17, Issue 1 , May 2023, , Pages 1-10
Abstract
Today, usage of brain-computer interface systems based on steady-state visual evoked potentials (SSVEPs) has been increased due to some advantages such as acceptable accuracy and minimal need for user training. Steady-state visual potentials are one of the most important patterns used in BCI systems, ...
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Today, usage of brain-computer interface systems based on steady-state visual evoked potentials (SSVEPs) has been increased due to some advantages such as acceptable accuracy and minimal need for user training. Steady-state visual potentials are one of the most important patterns used in BCI systems, which are generated in the occipital region of the brain by visual stimulation between 6 and 60 Hz. One of the effective methods for extracting the SSVEP frequency in BCI systems is called the Multiway Correlation Coefficient Analysis (MCCA) method, which is a tensorized version of the classical Correlation Coefficient Analysis (CCA) method and is based on multidimensional data.In this paper, inspired by the MCCA method, two new algorithms (PARAFAC-CCA and C-PARAFAC-CCA) have been proposed using the combination of CCA and PARAFAC decomposition. The purpose of the proposed algorithms is to improve the initial reference signal and achieve higher accuracy in SSVEP frequency detection in BCI systems. In the PARAFAC-CCA algorithm, after performing the PARAFAC decomposition on the multidimensional training data and obtaining the time component, the CCA method is implemented between the obtained time component and the sine-cosine reference signal, and the optimal reference signal is made from its output. Finally, the MLR algorithm is used between the EEG test data and the optimal reference signal in order to achieve the target frequency. The general steps of the C-PARAFAC-CCA algorithm are also similar to PARAFAC-CCA, with the difference that in the calculation of the time component, constrained PARAFAC is used in such a way that in each step of the ALS algorithm, CCA is applied once and the time component is improved. The efficiency of the proposed algorithms was investigated on the real data set and it was shown that compared to the MCCA method, the proposed algorithms have reached a higher average accuracy.
Hamed Sajedi; Seyed Ahmad Motamedi; Seyed Mohammad Firouzabadi
Volume -1, Issue 1 , June 2004, , Pages 3-14
Abstract
Auditory nerve fibers stimulating using electrical current with implanted electrodes are the basis of cochlear implant system. Therefore, expansion of current spread in volume conductor will change the electrical potential in a larger region. This expansion causes larger region stimulation and decreases ...
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Auditory nerve fibers stimulating using electrical current with implanted electrodes are the basis of cochlear implant system. Therefore, expansion of current spread in volume conductor will change the electrical potential in a larger region. This expansion causes larger region stimulation and decreases the accuracy and resolution of the stimulation in both the possibility of investigation of a particular region at Neural Response Telemetry (NRT) tests and also in hearing stimulation. Therefore, narrowing the width of stimulated region is the main goal in the selective stimulation. The conventional multi polar stimulation methods use lateral inhibitory electrode to form the spatial pattern of the electrical potential distribution for narrowing the stimulated region, but it needs to simultaneous stimulation of the electrodes, which is not available in implanted systems. In this paper, a new non-simultaneous multi-electrode stimulation method has been presented, which is based on applying the inhibitory pre-pulses by lateral electrodes. Inhibitory effect of the lateral electrodes pulses changes the initial conditions of the fibers and their thresholds. The results of simulations show that this method will solve the problem of simultaneous stimulation in conventional tri-polar stimulation methods and also is effective at controlling of stimulation area, comparing with tri-polar stimulation area, qualitatively and quantitatively.
Biological Computer Modeling / Biological Computer Simulation
Maryam Naghibolhosseini; Fariba Bahrami
Volume 2, Issue 2 , June 2008, , Pages 75-84
Abstract
This paper proposes a model to learn Farsi handwriting in different sizes based on human behavior. This model copies a human handwritten character with imitation. The imitation includes two stages of perception and action. During the perception, the information that is needed in order to generate the ...
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This paper proposes a model to learn Farsi handwriting in different sizes based on human behavior. This model copies a human handwritten character with imitation. The imitation includes two stages of perception and action. During the perception, the information that is needed in order to generate the character is extracted from the original pattern and during the action, the model generates a character similar to the original one. To rewrite a given character, first it is decomposed into the consecutive strokes. Each stroke is approximated by several linear subdivisions. We considered the slopes and lengths of these subdivisions as the features of a given handwriting. The model learns to write a character by learning to reproduce these features. These features are descriptive of the human handwriting behavior. The learning process becomes complete when all points of the character's trajectory have distance less than a specified distance with the original trajectory. This specified distance describes visual attention and is defined as the attention width. Attention width demonstrates the human accuracy during the different trials of learning. In our model, visual attention is adaptive and decreases as the learning progresses. After the completion of learning, Farsi letters with different sizes can be generated using only memory. In order to evaluate the performance of the model, the correlation between the original and simulated characters is used. The simulation results showed good performance of the model between different Farsi characters.
Bioceramics / Bioglasses
Mehrdad Davoudi; S. Mohammad Reza Shokouhyan; Mahdi Bagheri Rouchi; Masoud Abdollahi; Soha Bervis; Maryam Hoviat Talab; Mohamad Parnianpour
Volume 14, Issue 2 , July 2020, , Pages 81-96
Abstract
An open research question is how the central nervous system (CNS) to find a solution for the problem of redundancy or degree of freedom in the human shoulder motion control. We used time-varying synergy theory in which assumed that the relative activation between muscles is time-varying, to investigate ...
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An open research question is how the central nervous system (CNS) to find a solution for the problem of redundancy or degree of freedom in the human shoulder motion control. We used time-varying synergy theory in which assumed that the relative activation between muscles is time-varying, to investigate the combination of activation patterns of muscles in twelve 3-D hand-held exercises by Flexi-Bar using Anybody Technology software (A/S, Aalborg, Denmark). Using activation of 12 muscles and the moment across the joint as an input matrix for the optimization procedure to extract functional time-varying synergies, time delays and amplitude coefficients, the achieved 5 tonic and phasic synergies explained 79% of the data variation. Matching pursuit procedure and non-negative least square used to find timing shifts and amplitude coefficients respectively. Considering a new exercise out of the primary database, 60% of the activation patterns reconstructed using time-varying synergies. Although the extracted synergies seem to be directionally tuned, the results show that due to the same velocity in all exercises and also because the torque which that was applied due to the weight of the bar and arm on the joint is not significant, both timing shifts and separation phasic-tonic parts of the activation patterns provide no further explanation on CNS behavior and finding them causes unnecessary computational cost. Future study can focus on the comparison of synergies between two or more groups of exercises by the Flexi-Bar such as holding the bar vertically or horizontally with swinging it up and down or back and forth.
Biological Computer Modeling / Biological Computer Simulation
Mahmoud Amiri; Fariba Bahrami; Mahyar Janahmadi
Volume 4, Issue 2 , June 2010, , Pages 83-96
Abstract
Based on the neurophysiologic findings, astrocytes provide not only structural and metabolic supports for the nervous system but also they are active partners in neuronal activities and synaptic transmissions. In the present study, we improved two biologically plausible cortical and thalamocortical neural ...
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Based on the neurophysiologic findings, astrocytes provide not only structural and metabolic supports for the nervous system but also they are active partners in neuronal activities and synaptic transmissions. In the present study, we improved two biologically plausible cortical and thalamocortical neural population models (CPM and TCPM), which were developed previously by Suffczynski and colleagues, by integrating the functional role of astrocytes in the synaptic transmission in the models. In other words, the original CPM and TCPM are modified to integrate neuronastrocyte interaction considering the idea of internal feedback proposed by Iasemidis and collaborators. Using the modified CPM and TCPM, it is demonstrated that healthy astrocytes provide appropriate feedback control for regulating the neural activities. As a result, we observed that the astrocytes are able to compensate for the variations in the cortical excitatory input and maintain the normal level of synchronized behavior. Next, it is hypothesized that malfunction of astrocytes in the regulatory feedback loop can be one of the probable causes of seizures. That is, pathologic astrocytes are not any more able to regulate and/or compensate the excessive increase of the cortical excitatory input. Consequently, disruption of the homeostatic or signaling function of astrocytes may initiate the hypersynchronous firing of neurons. Our results confirm the hypothesis and suggest that the neuronastrocyte interaction may represent a novel target to develop effective therapeutic strategies to control seizures.
Biomedical Image Processing / Medical Image Processing
Ali Rafiei; Mohammad Hasan Moradi; Mohammad Reza Farzaneh
Volume 1, Issue 2 , June 2007, , Pages 83-93
Abstract
A new filter was designed and approved for speckle noise removal in sonography images. In this filter, a new idea is used by using neural network learning, fuzzy information and genetic algorithm optimization. The multi-layer perceptron neural network with binary weights is used in this filter. The neighborhood ...
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A new filter was designed and approved for speckle noise removal in sonography images. In this filter, a new idea is used by using neural network learning, fuzzy information and genetic algorithm optimization. The multi-layer perceptron neural network with binary weights is used in this filter. The neighborhood window of each pixel is used as input statistical features to estimate the noise level. Then it is fuzzificated and justified by simple fuzzy rules. The membership function width and network weights are optimized by on-line genetic algorithm. The on-line algorithm contains one individual, defined as a queen. In this algorithm, the next generation is created by using only the mutation operator. The performance of this filter was compared with the other speckle noise reduction techniques such as the median and homomorphic Wiener filters. Indeed, our proposed method is able to effectively remove speckle noises while preserving the quality of fine details in the image data better than the other methods. In this system, two classic and on-line GAs are used. The classic algorithm includes 50 strings. The results showed that both of the algorithms are the same in terms of noise reduction but the classic one is slower than the other one.
Bioelectrics
Amir Soleymankhani; Vahid Shalchyan
Volume 12, Issue 2 , September 2018, , Pages 85-96
Abstract
The extracellular recording from the brain's single neurons is known as a popular method in neuroscience and neuro-rehabilitation engineering. These recordings include the activity of all neurons around the electrode, for better use of which, spike sorting methods should be utilized to obtain the activity ...
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The extracellular recording from the brain's single neurons is known as a popular method in neuroscience and neuro-rehabilitation engineering. These recordings include the activity of all neurons around the electrode, for better use of which, spike sorting methods should be utilized to obtain the activity of single neurons. Based on the structural properties of the neuron, such as its dendritic tree, and the distance and direction of it relative to the electrode, it can be claimed that the form of its spike waveform is unique and constant. However, spike sorting under low signal-to-noise ratio (SNR) conditions is always accompanied with challenges. A spike sorting algorithm usually consists of three sections including the spike detection, feature extraction, and classification. In this paper, a method based on optimization of continuous wavelet coefficients is presented which is effective in low SNR values. In the proposed method, after the calculation of the parameterized wavelet coefficients, using the Euclidean distance and the area under the receiver operator characteristic curve, the best parameters were chosen to increase the separation of the features, so that a suitable scale was first found with the Euclidean distance criterion and then the translation parameter was obtained with the second criterion. In this research k-means algorithm was used for the clustering as a simple but efficient method. For evaluation, three simulated data sets were made in 9 different SNRs with a modeled background noise. The obtained results from simulated data showed that the optimization of parameters in continuous wavelet transform using the proposed algorithm could effectively improve the spike sorting performance compared to principal component analysis method.
Biomedical Signal Processing / Medical Signal Processing / Biosignal Processing
Reza Soleimani; Seyed Mpjtaba Rouhani
Volume 5, Issue 2 , June 2011, , Pages 89-103
Abstract
in this paper, a novel and effective algorithm for classification of important heart arrhythmia is presented. The proposed algorithm uses heart rate variation (HRV) signal which has better chaotic characteristics. In addition to commonly used linear time domain and frequency domain features, nonlinear ...
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in this paper, a novel and effective algorithm for classification of important heart arrhythmia is presented. The proposed algorithm uses heart rate variation (HRV) signal which has better chaotic characteristics. In addition to commonly used linear time domain and frequency domain features, nonlinear (chaotic) features are examined, too. To increase classification accuracy and facilitate learning, two techniques are used: a) extracted features are reduced by generalized discriminant analysis (GDA) and b) by a self organizing map (SOM), the most informant data are selected. Chaotic features help to improve diagnosis accuracy from 92% up to 97%. The results indicate the importance of GDA and SOM in efficiency of proposed algorithm. MLP, SVM and PNN classifiers are examined and compared. The proposed algorithm was able to diagnose 7 arrhythmias PVC, AFL, AF, CHB, LBBB, VF, VT and normal sinus rhythm (NSR) with 97.4% accuracy.
