Mohammad Firoozabadi

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received the B.Sc. Degree in electronics engineering from University of Tabriz, in 1987, the M.Sc. degree in electronics engineering from Amir-Kabir University of Technology, Tehran, in 1991, and the Ph.D. Degree in electrical engineering (biomedical engineering) from Tarbiat Modares University, Tehran, Iran, in1997. Dr. Firoozabadi currently serves as a Professor of Biomedical Engineering (Professor from 2009, Associate Professor 2003-2009, Assistant Professor 1998-2003 ) and Head of BioMedical Informatics Department at Tarbiat Modares University, and President of Iranian Society of Biomedical Engineering (2007-2014). He has published over 400 papers in peer reviewed journals and international conferences. He was Research Deputy Dean of Medical Sciences Faculty from 2009-2011, Manager of Apllied Research office(1997-2005) and Head of Medical Physics Department (2008-2010) at Tarbiat Modares University. He has received many awards including the Razie second rank prize of ``Invention, Innovation, Novation Group from the Iranian National Research Center of Medical Sciences (NRCMS) for ``design and implementation of isolated heart pacing, data logger and the signals analyzer system, in 2004. He has also received the awards of ``Outstanding Iranian Student, Country-wide in 1994 and Outstanding Student of Tarbiat Modarres University in 1993 and 1994. He has been the co-recipient of International Khwarizmi Award of ``Outstanding Research Team from the Iranian Research Organization for Science and Technology (IROST) for ``Tehran Cybernetic Arm project in 1990. His research and teaching interests include theory and application of bio-electromagnetics, Human-Machine Interaction, bioelectric phenomena and electrophysiology, bioinstrumentation, and biological signal processing. He has worked in Tehran Cybernetic Arm Group (Outstanding Research Team in Iran) since 1987 He is concurrently working in three branches: 1.use of Electromagnetic fieldsenergy for treatment of cancer, 2.use of Bio-Electromagnetic fieldsenergy for empowering the mind (the Passive-Neurofeedback Systems) and 3.development of the affective Human-Machine interfaces.

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Papers by Mohammad Firoozabadi

Factors Affecting Diabetes Type-1 Based On Individuality And Self-care Evaluation

Personalized Medicine Journal, Sep 1, 2019

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Recognition of Positive, Negative and Neutral Emotions Using Brain Connectivity Patterns

2017 24th National and 2nd International Iranian Conference on Biomedical Engineering (ICBME), 2017

There are various resources inside the brain. Brain activities are the result of these sources or... more There are various resources inside the brain. Brain activities are the result of these sources or the result of their connectivity. Therefore, any special emotion should also be the result of various connectivity chains among the brain's resources. Studying this connectivity chains could help us recognize the corresponding emotions. The aim of this paper is to find interaction patterns in positive, neutral and negative emotions, and to recognize different types of emotions. We have used DEAP data in this project. These datasets were gathered from 32 volunteers, half of whom were women. Playing different types of music, caused them to experience special emotions, and their brain signals were recorded simultaneously. Music videos belonged to three different classes: positive, neutral and negative. After preprocessing the signals, we have achieved the connectional characteristics among the various channels, including causal features in various delays. Utilizing Davis-Bouldin Method, we obtained the sub-group of the optimal features. To evaluate the obtained results, we used SVM and KNN clustering methods. The final classified results, describes more favorable performance of interactional patterns and show the fact that connectional features can classify the classes in two arousal and valence with accuracy %79.7 and %88.2 respectively, which had %6 and %12.54 increase with respect to other traditional features.

Recognition of Music-Induced Emotions Based on Heart-Brain Connectivity

2017 24th National and 2nd International Iranian Conference on Biomedical Engineering (ICBME), 2017

Emotion recognition using physiological signals has received a great deal of attention in recent ... more Emotion recognition using physiological signals has received a great deal of attention in recent years. Local features of signals have been more commonly used in this field of research. Brain connectivity measures have also been applied to emotion recognition in last few years. Considering the extensive neural interactions between the brain and heart and the intrinsic nervous system of the heart, and bearing in mind the different autonomic nervous system activity in various emotional states, we expect that the connectivity between the heart and brain can be measured as a discriminative feature for the purpose of emotion recognition. In the present paper, we investigate the possibility of recognizing four music-induced emotions on the basis of functional connectivity between the heart and brain. One of the discriminating features in multichannel/multisource signal processing is Mutual Information. Herein, mutual information is estimated between the power of three channels of forehead biosignals in six sub-bands and the normalized high frequency component of HRV. Subject-dependent and subject-independent classification studies were carried out using SVM classifier, which resulted in an average accuracy of 95.7% and 86.9%, respectively. The promising results suggest that the amount of information which our brain and heart provide about each other is different in each emotional state and may be used to distinguish between them.

Effect of low frequency rTMS on stiffness of joints of the upper limbs in hemiplegic patients

Scientific journal of Kurdistan University of Medical Sciences, 2014

ABSTRACT Background and Aim: Disabilities of the upper limbs due to stroke can lead to more prolo... more ABSTRACT Background and Aim: Disabilities of the upper limbs due to stroke can lead to more prolonged functional problems in comparison to lower limb disabilities, and fewer numbers of investigations have been performed on this problem. Use of magnetic stimulation to produce induction currents in the brain for therapeutic purposes is a new method. The aim of this study was to determine the effects of rTMS plus routine rehabilitation on hand stiffness, and compare them with pure routine rehabilitation program in hemiplegic patients. Method and material: 12 hemiplegic patients were assigned randomly to two groups. Control group, received rehabilitation program with placebo magnetic stimulation, and experimental group, received magnetic stimulation with routine rehabilitation program, 3 times/week for 10 sessions. Using Wrist Robo Hab,we assessed joint stiffness during flexion-extension of the wrist, and supination-pronation of forearm before and after treatment. Results: In the control group we did not find any significant decrease in the resistance of flexor (p=0.207) and pronator (p=0.291) muscles. But in the experimental group the resistance of flexor (p=0.009) and pronator (p=0.035) muscles decreased significantly. Conclusions: rTMS can decrease stiffness of the upper limb joints and can be used in chronic hemiplegic patients, when conventional treatments are not effective. Keywords: Stroke, Transcranial Magnetic Stimulation, Rehabilitation,Stiffness,Wrist Robo Hab

Gamma-Enhancing Neurofeedback at Oz-EEG Channel Toward Visual Feature Binding and Fluid Intelligence Enhancement

Study aim: To study Gamma-enhancing neurofeedback learning process and evaluate its efficacy on v... more Study aim: To study Gamma-enhancing neurofeedback learning process and evaluate its efficacy on visual feature binding and fluid intelligence Sample size: 18 healthy female students (mean age: 24.24 ± 1.94 years) Dataset: ----------- 1- Demographics: 18 subjects, Age, BMI, Weight, Height, Handedness, GPA 2- IQ measure: 18 subjects, Pretest and posttest sessions 3- Visual feature binding measure: 18 subjects, Pretest and posttest sessions, Response time and Error rate 4- 4 activity baseline EEG: 18 subjects, Pretest and posttest sessions, Tasks: Eyes open, Eyes closed, Auditory sensory attentiveness, Cognitive effort 5- Neurofeedback training EEG: 8 subjects, 8 training sessions, Eyes closed baseline EEG recorded before and after training in each session, EEG recorded during training in each session

The role of 217-Hz ELF magnetic fields emitted from GSM mobile phones on electrochemotherapy mechanisms

Electromagnetic Biology and Medicine, 2020

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Effects of tripolar TENS on slow and fast motoneurons: a preliminary study using H-reflex recovery curve method

Electromyography and clinical neurophysiology

This study aimed at examining the effect of tripolar TENS of vertebral column on the activity of ... more This study aimed at examining the effect of tripolar TENS of vertebral column on the activity of slow and fast motoneurons on 10 healthy non-athlete women aged 22.7 +/- 2.21 yrs. H-reflex recovery curve of soleus (slow) and gastrocnemius (fast) muscles were recorded before and after applying tripolar TENS. For recording of this curve, rectangular paired stimuli were applied on tibial nerve (with 40-520 ISI, frequency of 0.2 Hz and pulse width of 600 micros). Our findings showed that maximum H-reflex recovery in gastrocnemius muscle appeared in the shorter ISI, while in soleus muscle, it appeared in the longer ISI and its amplitude slightly decreased after applying tripolar TENS. It is suggested that tripolar TENS excites not only the skin but also Ia and Ib afferents in the dorsal column. A Synaptic interaction of these afferents in spinal cord causes the inhibition of type I MNs and facilitation of type II MNs. This effect can be used in muscle tone modulation.

Paper: SHORT-TERM EFFECTS OF WHOLE-BODY VIBRATION TRAINING ON NEUROMUSCULAR ACTIVITY OF MUSCLES IN IMPORTANT AREA IN RESPECT OF OSTEOPOROTIC FRACTURES IN MAXIMAL VOLUNTARY ISOMETRIC CONTRACTION IN YOUNG HEALTHY WOMEN

Effects of 950 MHz mobile phone waves on hippocampal long-term potentiation

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Parametric Modeling of Nerve Cell under the Sinusoidal Environmental 50 Hz Extremely Low Frequency Magnetic Fields

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Comparison of low frequency rTMS and rehabilitation combination therapy with rehabilitation on motor function and grip force of the upper limbs in hemiplegic patients

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An evolutionary network model of epileptic phenomena

Neurocomputing, 2011

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Evaluating variability of frequency features of EEG signals during mental fatigue

2010 17th Iranian Conference of Biomedical Engineering (ICBME), 2010

Abstract The brain regions involved in variation of mind activity from attention to mental fatigu... more Abstract The brain regions involved in variation of mind activity from attention to mental fatigue are considered to reduce the number of recording EEG channels for detecting brain activities. In this study, subjects performed a visual attentive task for about 45 minutes without rest and during task, EEG signals, subjective levels of fatigue and performance measures were recorded. Frequency features of EEG signals extracted by using spectrogram and discrete wavelet were transformed to distinguish different levels of ...

A New Approach for EMG Decomposition Based on Overlaps Solution

2007 15th International Conference on Digital Signal Processing, 2007

Abstract In this paper a decomposition algorithm is introduced that uses three conventional metho... more Abstract In this paper a decomposition algorithm is introduced that uses three conventional methods simultaneously in different input channels for obtaining better results and resolving overlaps. The signal is recorded through one channel and the same signal is divided to gain different channels information. The firing patterns of the motor units obtained after decomposition in three channels in specific time domain are used for overlap solution. In order to verify this method, EMG signal is simulated in such away that it could resemble ...

The antitumor efficiency of combined electrochemotherapy and single dose irradiation on a breast cancer tumor model

Radiology and Oncology, 2012

Study of chaotic behavior of tremor of some Parkinsonians under deep brain stimulation

Australasian Physical & Engineering Sciences in Medicine, 2011

Deep Brain Stimulation, functional neurosurgery for Parkinson&amp;amp;amp;amp;amp;amp;amp;amp... more Deep Brain Stimulation, functional neurosurgery for Parkinson&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;#39;s disease treatment, has three parameters: frequency, pulse width, and voltage. They should be selected appropriately to achieve an effective treatment. In this research, we studied some nonlinear dynamics of Parkinsonian tremor under Deep Brain Stimulation. We recorded finger tremor signals of 8 patients at different voltages as data. To study tremor behavior variation at different voltages, we extracted some nonlinear dynamic features of the data. These features were: embedding space, correlation dimension, largest Lyapunov exponent, entropy and variance of embedding space along x, and y axes. We found that the largest Lyapunov exponents were positive and correlation dimensions of data were not integer, and also all embedding spaces at different voltages had attractors. These observations show that the tremor signal has chaotic behaviors. Also, embedding space variance along x, and y axes in all cases and entropy in the most of them had minimum values at highest voltages in comparison with other voltages. We plotted bar graphs of embedding space variances along x, and y axes at every patient&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;#39;s recorded voltages. A reduction procedure was seen after a threshold in all graphs. As a result, all the chaotic features change with the voltage variation. In the future, it could be possible to select the optimum stimulation voltages by using chaotic features of tremor.

A fast approximate method for predicting the behavior of auditory nerve fibers and the evoked compound action potential (ECAP) signal

Journal of Medical Signals & Sensors

Background: The goal of the current research is to develop a model based on computer simulations ... more Background: The goal of the current research is to develop a model based on computer simulations which describes both the behavior of the auditory nerve fibers and the cochlear implant system as a rehabilitation device. Methods: The approximate method was proposed as a low error and fast tool for predicting the behavior of auditory nerve fibers as well as the evoked compound action potential (ECAP) signal. In accurate methods every fiber is simulated; whereas, in approximate method information related to the response of every fiber and its characteristics such as the activation threshold of cochlear fibers are saved and interpolated to predict the behavior of a set of nerve fibers. Results: The approximate model can predict and analyze different stimulation techniques. Although precision is reduced to <1.66% of the accurate method, the required execution time for simulation is reduced by more than 98%. Conclusion: The amplitudes of the ECAP signal and the growth function were investigated by changing the parameters of the approximate model including geometrical parameters, electrical, and temporal parameters. In practice, an audiologist can tune the stimulation parameters to reach an effective restoration of the acoustic signal.

Recognition of two emotional states of joy and sadness using phase lag index and SVM classifier

2016 23rd Iranian Conference on Biomedical Engineering and 2016 1st International Iranian Conference on Biomedical Engineering (ICBME), 2016

Due to the preceding studies in recent years, emotion recognition has not been done only by using... more Due to the preceding studies in recent years, emotion recognition has not been done only by using local features of a single channel. Since the process of hearing and understanding it, requires the cooperation of different brain regions, it is expected that investigation of brain connectivity among channels can be an appropriate tool for emotion recognition beside the local analysis of each channel. The aim of this research was to evaluate the possibility of recognition of two music induced emotional states, joy and sadness, using functional connectivity features extracted from EEG signals. To achieve this goal, phase lag index (PLI) features of every channel pairs (6 features) and support vector machine (SVM) algorithm were used. Based on the results, among all extracted features, the most distinction between the emotional states of joy and sadness was obtained using the connectivity features of channel pair C3−F3 and also channel pair C4−F4 with the classification accuracies of 68.18% and 54.55%, respectively. According to the fact that these two features are related to the connectivity of two similar channels in two hemispheres, it seems that the inter-hemispheric connections are more related to the emotional states of joy and sadness rather than the intra-hemispheric connections. Furthermore, the classification precision was higher when using 6 features simultaneously (80%). This result represents the importance of brain network analysis.

The Impact of Data Mining on Prediction of Renal Transplantation Survival and Identifying the Effective Factors on the Transplanted Kidney

Making EEG Experiments Retrievable for Research Purpose: The Preliminary Experience of Standardization of EEG Data in Iranian Brain Mapping Biobank

Iranian Journal of Radiology, 2019

Background: Increasing technological advances in the field of biological signal recording, along ... more Background: Increasing technological advances in the field of biological signal recording, along with diverse available data storage and sharing facilities, has made it much easier for researchers to access extensive biological data for use in their studies. Today, data once recorded in a study can be repeatedly reused by other researchers through access to shared databases. Access to biosignal pools, on the one hand, can save considerable energy and reduce costs by preventing duplicate studies. On the other hand, it improves opportunities for meta-analysis and in-depth studies using diverse datasets with greater statistical power, which provides more reliable results, as well as new insights into biological issues. However, the lack of some agreed-upon data standardization and consistency across the research community creates some barriers to reusing data. Data from different studies often have different formats and structures, which may impose extensive data reformatting for meta-...