Maryam Saidi; Seyed Mohammad Firoozabadi
Volume 13, Issue 4 , December 2019, , Pages 303-314
Abstract
Transcranial Direct Current Stimulation (tDCS) is a non-invasive brain stimulation technique that is affordable and easy to operate compared to other neuromodulation techniques. Despite this method is promising in treating neurological diseases and enhancing cognitive functions, the precise mechanism ...
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Transcranial Direct Current Stimulation (tDCS) is a non-invasive brain stimulation technique that is affordable and easy to operate compared to other neuromodulation techniques. Despite this method is promising in treating neurological diseases and enhancing cognitive functions, the precise mechanism of the effect of this sub-threshold stimulation has not been understanded well. Understanding the mechanism is important in designing the proper protocol and system for the brain's electrical stimulation. The aim of this paper is to identify this mechanism with the neural modeling approach. As the results of some physiological studies have shown that under tDCS, sudden calcium signaling associated with calcium signaling of astrocyte cells in the brain are found, in the proposed model, this cell is considered as well as the main neurons and interneurons. The purpose of this model is to simulate the effect of tDCS on cortical activity related to the evoked response potential (ERP) and to compare with the actual results of previous experimental studies on rats. The results show that this model can simulate all the evidence of experimental studies, while the proposed purely neuronal model in previous studies could not simulate all the evidence.
Biomedical Signal Processing / Medical Signal Processing / Biosignal Processing
Zahra Tabanfar; Seyed Mohammad Firouzabadi; Zeynab Shankaei; Giv Sharifi; Kambiz Novin; Anahita Zoghi
Volume 10, Issue 3 , October 2016, , Pages 211-221
Abstract
In this research, we analyzed the EEG signals of patients with brain tumor and healthy participants in order to study the effects of brain tumor on brain signals and also the feasibility of brain tumor detection using EEG signals. For this reason, EEG signals of four channel F3, F4, T3 and T4 from 5 ...
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In this research, we analyzed the EEG signals of patients with brain tumor and healthy participants in order to study the effects of brain tumor on brain signals and also the feasibility of brain tumor detection using EEG signals. For this reason, EEG signals of four channel F3, F4, T3 and T4 from 5 patients with brain tumor and 4 healthy participants were recorded. After preprocessing, linear features in time and frequency domains and nonlinear ones such as fractal dimensions and entropies were extracted. Afterwards, the differentiation between2 groups was analyzed using Davies-Bouldin Index, LDA, KNN and SVM classifiers. According to the results of Davies-Bouldin Index, RMS, Theta Absolute Power, Approximate Entropy and Sample Entropy features in resting state with eyes closed and RMS and Theta Absolute Power features in resting state with eyes opened, had the most distinction between the two groups. In this stage classification of two groups using single features was done and the most accuracy of 88.89% was obtained for RMS feature in resting state with eyes closed. At the end, classification of two groups using all selected features was conducted and the maximum accuracy of 82.54% was obtained for RMS, Theta Absolute Power, Approximate Entropy and Sample Entropy features in resting state with eyes closed. According to the results, EEG linear features have a good capability of detecting brain tumor. As these features are simple and have low computational complexity, they can be used in online applications especially for periodic screening tests.
Neuro-Muscular Engineering
Mohsen Abedi; Majid Mohammadi Moghaddam; Mohammad Firoozabadi
Volume 9, Issue 1 , April 2015, , Pages 33-48
Abstract
In this paper the simulation of pathological behavior in gait locomotion of central nervous system (CNS) diseases and effects of rehabilitation techniques are investigated. These simulations noticeably deepen the knowledge of researches in neurorehabilitation realm about the neuroscientific basis of ...
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In this paper the simulation of pathological behavior in gait locomotion of central nervous system (CNS) diseases and effects of rehabilitation techniques are investigated. These simulations noticeably deepen the knowledge of researches in neurorehabilitation realm about the neuroscientific basis of CNS treatment after a neural disorder. However, only a limited number of these simulations have been proposed in the previous works that issued some aspects of CNS diseases. Due to this limitation, in this paper, a more efficient simulation has been done on pathological behavior of neural disorders with including the brain signal disruption in the models. To do this, combinations of neural reflexes and central pattern generator (CPG) has been incorporated in the neuromuscular system and examined on two different msculoskeletal system containing two leg-one segment and one leg-two segment systems. Then, the locomotion of hemiplegia and paraplegia patients are simulated by inserting a malfunction in the supraspinal signal coming into the CPG. Moreover, the effects of rehabilitation effects on paraplegia patients have been investigated and qualititatively compared to the experimental data.
Biomedical Signal Processing / Medical Signal Processing / Biosignal Processing
Mahdi Khezri; Seyed Mohammad Firoozabadi; Seyed Ahmad Reza Sharafat
Volume 8, Issue 4 , February 2015, , Pages 339-358
Abstract
In this study, we propose decision level fusion of multimodal physiological signals to design an affect identification system using the MIT database. Four types of physiological signals, including blood volume pressure (BVP), respiration rate (RSP), skin conductance and facial muscles activities (fEMG) ...
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In this study, we propose decision level fusion of multimodal physiological signals to design an affect identification system using the MIT database. Four types of physiological signals, including blood volume pressure (BVP), respiration rate (RSP), skin conductance and facial muscles activities (fEMG) were utilized as affective modalities. To collect the above-mentioned database, researchers used personalized imagery to elicit the desired affective states from a single subject and recorded the corresponding physiological signals simultaneously. In this study, the best subset of features for each signal was determined using previously calculated time and frequency domain features. To this end, sequential floating forward selection (SFFS) and RELIEF feature selection algorithms were evaluated. A new feature set, formed by concatenating the selected features, was partitioned into three subsets. Each subset was then fed into a classifier to identify the desired affective states. The majority voting method was applied to fuse the results obtained by the subsystems. Three types of classification methods, namely SVM, LDA and KNN were evaluated to design an affect identification system. The results showed remarkable performance from the system in identifying the desired scenarios with an acceptable accuracy and speed of response. Using the RELIEF feature selection method, along with SVM as a classifier, an overall recognition accuracy of 93.8% was obtained, which is better than the results reported with the use of the above-mentioned database so far.
Biomedical Signal Processing / Medical Signal Processing / Biosignal Processing
Mohsen Naji; Seyed Mohammad Firouzabadi; Sedighe Kahrizi
Volume 7, Issue 1 , June 2013, , Pages 13-20
Abstract
The collected electromyogram (EMG) signals from trunk musculature (e.g., rectus abdominis and external oblique muscle) are often contaminated with the heart muscle electrical activity (ECG). This paper introduces a novel method, the Empirical Mode Decomposition, for elimination of ECG contamination from ...
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The collected electromyogram (EMG) signals from trunk musculature (e.g., rectus abdominis and external oblique muscle) are often contaminated with the heart muscle electrical activity (ECG). This paper introduces a novel method, the Empirical Mode Decomposition, for elimination of ECG contamination from EMG signals. The method is compared to a Butterworth high pass filtering. Results obtained from the analysis of generated and experimental EMG signals show that our method outperforms the high pass filtering for elimination of ECG contamination from trunk EMG signals.
Biomedical Signal Processing / Medical Signal Processing / Biosignal Processing
Nasrin Shourie; Seyed Mohammad Firouzabadi; Kambiz Badie
Volume 7, Issue 4 , June 2013, , Pages 321-331
Abstract
In this article, differences between multichannel EEG signals of artists and nonartists were investigated during visual perception and mental imagery of some paintings and at resting condition using scaling exponent. It was found that scaling exponent is significantly higher for artists as compared to ...
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In this article, differences between multichannel EEG signals of artists and nonartists were investigated during visual perception and mental imagery of some paintings and at resting condition using scaling exponent. It was found that scaling exponent is significantly higher for artists as compared to nonartists during the three mentioned states, suggesting that scaling exponent may reflect the influence of artistic expertise. No significant difference in scaling exponent was observed between the visual perception and the mental imagery tasks. In addition, the two groups were classified using scaling exponent of channel C4 and Neural Gas classifier during the visual perception, the mental imagery and the resting condition. The average classification accuracies were 50%, 58.12% and 70%, respectively. The obtained results suggest that discriminability in scaling exponent decreases during the performance of similar cognitive tasks.
Biomedical Signal Processing / Medical Signal Processing / Biosignal Processing
Mehdi Abdossalehi; Ali Motie Nasrabadi; Seyed Mohammad Firouzabadi
Volume 7, Issue 2 , June 2013, , Pages 143-153
Abstract
In this study, electroencephalogram (EEG) signals have been analyzed in positive, negative and neutral emotions. Here it is supposed that the brain has different independent sources during an emotional activity which will be extractable by Independent Component Analysis (ICA) algorithm. For resolving ...
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In this study, electroencephalogram (EEG) signals have been analyzed in positive, negative and neutral emotions. Here it is supposed that the brain has different independent sources during an emotional activity which will be extractable by Independent Component Analysis (ICA) algorithm. For resolving the illposeness problem of extracted components by ICA algorithm, first these sources were sorted by Shannon entropy and then the features of Katz fractal dimension and the first local minimum of the mutual information based on the time delay (tau) have been extracted for representing determinism. The results show that the determinism ratio of the sorted sources has significant difference during the time in three emotional states: positive, negative and neutral. The determinism ratio increases in neutral, negative and positive emotional states, respectively.
Bioelectromagnetics
Hadi Tavakoli; Ali Motie Nasrabadi; Seyed Mohammad Firouzabadi; Mehri Kaviyani Moghaddam
Volume 6, Issue 2 , June 2012, , Pages 123-131
Abstract
During recent years, the environment has been enormously changed by the wide range of magnetic fields. Therefore, comprehensive studies are being done for investigating their biological effects. The effects such as inhibition of bioelectric activity of neurons which is shown by evidence, like decreasing ...
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During recent years, the environment has been enormously changed by the wide range of magnetic fields. Therefore, comprehensive studies are being done for investigating their biological effects. The effects such as inhibition of bioelectric activity of neurons which is shown by evidence, like decreasing in the firing frequency or decreasing in the amplitude of action potential, have been shown. To notify and investigate these effects, the theory of “biological windows” have been proposed and considered. The effects of amplitude and/or frequency of magnetic field have been pointed in some research. In this study, regarding the behavior of nervous system, which has non-linear dynamic behavior, we study the behavior of nervous system under exposure to magnetic field. We investigate whether the low frequency field is able to affect the dynamic of nerve cells and to have influence on non-linear features of signal. We used 6 environmental intensities and 6 cells have been used in each intensity, and by calculating some of non-linear features of action potential such as Higuchi Dimension and Return map of signal, during the time and in some different intensities of magnetic fields, It was observed that all intensities magnetic fields lead to increasing in Higuchi Dimension and increasing in the scattering of the Return map of signal. Of course these effects has been more observed in the middle band of frequency which has been confirmed by the theory of ‘frequency window’ effect of magnetic fields, which it has been noticed and discussed in last two decades.
Biological Computer Modeling / Biological Computer Simulation
Siamak Haghipour; Seyed Mohammad Reza Hashemi Golpayegani; Seyed Mohammad Firouzabadi; Sirous Momenzadeh
Volume 3, Issue 3 , June 2009, , Pages 227-241
Abstract
The procedure of pain formation embarks on primary sensory neurons and then ends in central nervous system which is the first stage in the dorsal horn of the spinal cord. Nowadays the great challenge of some researchers for pain control has been to elucidate the mechanisms that are able to switch the ...
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The procedure of pain formation embarks on primary sensory neurons and then ends in central nervous system which is the first stage in the dorsal horn of the spinal cord. Nowadays the great challenge of some researchers for pain control has been to elucidate the mechanisms that are able to switch the state of the dorsal horn of the spinal cord from an unwanted state to a favorite one. In order to achieve such an aim, a model of the function of the dorsal horn of the spinal cord is extracted in order to be able to control the created pains with changing the parameters of the aforementioned model. In this study a cybernetic model is presented with the aid of bifurcation methodologies and reconstructing the dynamics linked with the process of pain formation via clinical experiment that can express different states in the dorsal horn of the spinal cord as normal, suppressed, sensitized, the functionality of memory, the effect of other primary afferents and the effect of descending signals. Input signals in this model consist of thermal stimulation degree proportional to action potential firing rate from Ab afferents, inhibitory descending signals from midbrain and inhibitory or excitatory descending signal from thalamus and cortex and the output signal is the action potential firing rate from transmission cells in dorsal horn of the spinal cord proportional to pain level have been sensed. The significant and remarkable characteristic of this model is applying a cybernetical model based on a sequence of input-output data which can obviate the drawbacks of other models in which simplification and reduction of terms reduce the operation of components of a system. On the other hand, unlike previous models which have been modeled based on membrane (slow) potential, this model is based on the action potential firing rate from transmission cells of the dorsal horn of the spinal cord that has the adaptability with cellular recording as well as having a higher accuracy.
Biomedical Signal Processing / Medical Signal Processing / Biosignal Processing
Kianoush Nazarpour; Ahmad Reza Sharafat; Seyed Mohammad Firouzabadi
Volume 1, Issue 3 , June 2007, , Pages 189-199
Abstract
A novel approach to surface electromyogram (sEMG) signal classification using its higher order statistics (HOS) is presented in this study. As the probability density function of the sEMG during isometric contraction in some cases is very close to the Gaussian distribution, it is frequently assumed to ...
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A novel approach to surface electromyogram (sEMG) signal classification using its higher order statistics (HOS) is presented in this study. As the probability density function of the sEMG during isometric contraction in some cases is very close to the Gaussian distribution, it is frequently assumed to be Gaussian. As this assumption is not valid when the force is small, in this paper, we consider the non-Gaussian characteristics of the sEMG, and compute the second-, the third- and the fourth order statistics of the sEMG as its features. These features are used to classify four upper limb primitive motions, i.e., elbow flexion (EF), elbow extension (EE), forearm supination (FS), and forearm pronation (FP). We used the sequential forward selection (SFS) method to reduce the number of HOS features to a sufficient minimum while retaining their discriminatory information, and apply the Knearest neighbor method for classification. Our approach is robust against statistical variations in noise, and does not require additional computations compared to existing methods for providing high rates of correct classification of the sEMG, which makes it useful in devising real-time sEMG controlled prostheses.
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.
Neuro-Muscular Engineering
Amin Mahnam; Seyed Mohammad Firouzabadi; Seyed Mohammad Reza Hashemi Golpayegani
Volume -1, Issue 1 , June 2004, , Pages 65-76
Abstract
In recent years, various methods have been suggested to improve selectivity in electrical stimulation of neural fibers or cells. One of these methods is the use of depolarizing under-threshold prepulse to selectively stimulate fibers far from the electrode, without excitation of nearer fibers. In this ...
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In recent years, various methods have been suggested to improve selectivity in electrical stimulation of neural fibers or cells. One of these methods is the use of depolarizing under-threshold prepulse to selectively stimulate fibers far from the electrode, without excitation of nearer fibers. In this paper, by implementing a nonlinear model of neural fiber and simulating electrical stimulation of the model, the effect of changes in various parameters of rectangular and stepwise prepulses on the range of applicability of this technique in selective stimulation of fibers in different distances from the electrode and with different diameters has been studied. This study has led to suggest a new waveform for the prepulse; ramp prepulse. The applicability of this prepulse has been studied also. The superiority of this prepulse in comparison with previous suggested ones has been shown. Using this prepulse, it is possible to stimulate selectively fibers in broader range of distances and diameters. Therefore in stimulating neural fibers in spinal cord or peripheral fibers or even neural fibers of special senses, the use of this prepulse can improve distinguishability of fibers in their stimulation.
