Areas of Interest:
Biological System Control; Motor Control; Intelligent Control; Biological System
Modeling; System Identification; Bio-instruments
- Ph.D. Biomedical Engineering, University of Saskatchewan, CANADA, 1996. (My Thesis)
- M.Sc. Electrical Engineering, Amirkabir University of Technology, IRAN, 1989.
- B.Sc. Electrical Engineering, Amirkabir University of Technology, IRAN, 1984.
- Digital Control
- Modeling and Simulation
- Introduction to Mathematics and Programming
Graduate Students and Theses Supervised
1. Jafari, A. H., Realization of the Cerebellum Model Using MPIC , 1998
2. Ebrahimzadeh, A. Control of Joint Sub-Movement Using MPIC, 1999.
3. Saalehpour, S. Joint Movement Control Using Fuzzy Logic and MPIC 1999.
4. Asadpour, V. Blood Flow Measurement and Simulation Using Laser Doppler and Monte Carlo Method 2000.
5. Kouhsaari, A. H. A Statistical Modeling of DT Gyro Drift Rate, 2001
6. Kordari, K. An Optimal Algorithm for On-Line Joint Impedance Tuning, 2001.
7. Taebi, M. Intelligent Control of Mean Arterial Pressure and Cardiac Output in CHF Patients, 2001.
8. Nosratri, S. Design of a Robust Controller for Haptic Systems, 2002.
9. Nazemzadeh, Design of a MRI Video Capture PC Based Board, 2002.
10. Yousefian, A. R. Design of a Measurement System for Ultrasonic Waves, 2002.
11. Noor, A. A. Design of a Robust Control System for DT Gyro, 2003.
12. Karimian, M. Application of MPIC in Analysis of Human Walking on Rough Terrains, 2003.
13. Moosavi Firdeh, Design of a Controller for a Combustion Chamber Engine, 2003.
14. Jonmaleki, M. Design and Manufacture of Magnetic Field Driver for Rheological Viscometer, 2004.
15. Jonfeshan, K., Infant Identification Using Footprint, 2004.
16. Ahouraee, A. Modeling of Neonatal Jaundice Diagnosis and Treatment, 2004.
17. Mahboobi, R., Application of MPC in FES Assisted Standing up Paraplegia Patients, 2004.
18. Shalbaf, R., Blood Velocity Measurement Using Laser Doppler, 2005.
19. Gitisarand, Modeling and Control of a Power Plant Boliler Using MPC; 2005.
20. Mashhadi-Malek-M., Modeling of Parkinson Disease Using Neural Networks, 2006.
21. Talebian-Moghadam, H., Diagnosis and Prediction of Neonatal Jaundice, 2006.
22. Saberi-Moghadam, S., Prediction of Temporal AF, 2006.
23. Ahmadi-Pajouh, M. A., Modeling of Human Path Finding Using Model Predictive Control, 2007.
24. Daliri, A, Speech Motor Control Modeling, 2008.
25. Yavari, F., Gene regulatory network modeling using time series microarray data,2005
26. Bamdadian, A., A Model Based Predictive Controller (MPC) for Administrating the Drug for Anesthesia, 2005
27. Taghizadeh, B., Modeling of Human Path Planning Procedure, Using Model Based Predictive Control, 2006
28. Kianifar R., Modeling of Human Decision Making Using Model Predictive Control and Markov Decision Process, 2006
29. Borjkhani M., Modeling of Human Hand Writing Using Model Predictive Control, 2006
30. Saeedi S. Modeling Spatial Memory and Its Neuronal Mechanisms, 2006
31. Abouei V., Model Predictive Controller Design for Hemodialysis System Using Patient Blood Pressure Biofeedback
32. Rezvanian S.,
33. Lahimgarzadeh N., Functional Modeling of a Driver Motor Behavior Using Model Predictive Control
34. Marghi Y., Human Path Planning Modeling in Driving Task Using Predictive Guidance and Biological Findings
35. Azizi Sh., Development of Computer Aided speech therapy software using signal processing, modeling, and speech recognition
36. Saeidi M., Modeling of Visual and Proprioceptive Multisensory Integration by Considering Learning
37. Hajian R., Modeling and Control of Unwanted tremors Using Electrical Stimulation of Muscle, 2011
38. Irvani B., Feature extraction and modeling of 6-OHDA lesioned rats after deep brain stimulation, 2011
39. Motie-Sharh M.A., A Model for an investigation on focus and shift attention, 2011
40. Arjmand M., Design and Simulation of PC-Controlled Robot for Stiffness Measurement, 2011
41. Mesbah, S., Dynamical Characterization and Feedback Control of Oscillatory Neural Systems, 2012
42. Sharifi, M.A., Biped locomotion control and adaptation using central pattern generators, 2012
43. Sadeghi, M., Gait disorder modeling in Parkinson's disease, 2012
44. Ahmadi Sh., Modeling of Visual and Proprioceptive Multisensory Integration in Autistic Patients, 2012
45. Kaboodvand N., Extraction and Analysis of Muscle Synergies During Rehabilitation, 2012
46. Safarbali B., Analysis of Multiple Sclerosis neural symptoms based on electrical modelling, 2012
47. Mahdavi S., "Modeling and simulation of brain transcranial DC stimulation using finite element", 2013
48. Gharenazi Faam M., "Improve facial action units detection based on sparse learning", 2013
49. Davanipour S., A model for the integration of sensory information in the human brain considering learning process and internal models",2015
50. Naderkaam Firoozi F., "Modeling of an object Movement Trajectory Anticipation in human",2016
51. Nikkhai Z., "Identification of sensory and motor components related to change in hands position perception after visuomotor adaptation", 2016
1. Darainy M., M. Study and Modeling of Learning Process in Skill Movements Using MPIC Approach.2005.
2. Asadpour, V. "Human Identification Using the Face Muscles Movements", 2006.
3. Khayati, R, Qualification of MS Lesions Using Fractal Analysis", 2008.
4. Sarbaz, Y., Modeling Parkinsons disease Using Chaos theory, 2006-2011
5. Ahmadi-Pajouh, M.A., Estimation of Elbow Stiffness Using Kinematic Features and EMG Signal, 2011
5. ZendehRouh, S., Modeling action selection in human brain, 2012
6. Bakouie F., computational development of dynamic core hypothesis in consciousness, 2012
7. Yavari, F., Improving modeling of human motor control learning using model predictive control and Impedance control, 2013
8. Mohammadian A., "Improving the recognition system of face action units based on style", 2014
9. Falaki A., Modifications of Muscle Synergies according to the Learning in CNS and Its Relation to Impedance Control
10. Baghdadi G., "Modeling the function of human attention control system and investigating its disorder in ADD children"
11. Esmailpoor Z., "Modeling the effects of trans-cranial electrical stimulation of neuronal assemblies"
12. Novin S., "Computational Modeling of divided Visual Attention"
1. Design and Manufacture of Laser Doppler Blood Flowmeter, 2006 (Director).
2. Design and Manufacture of Analog Computer, 1989 (Director).
3. Strategy of IDRO (Industrial Development and Renovation Organization of Iran) in Biomedical Engineering, 2005 (Co-worker).
4. Designing, Manufacturing, and Clinical Feasibility Assessment of a Test Sample of Transcranial Direct & Alternative Current Stimulator Programmable with PC, 2016
1. Vice-President of Iranian Biomedical Eng. Society (2004-2009).
2. Graduate Coordinator (2007-2008).
3. Chair of Bio-electric Group (2005-2006).
4. Head of Biomedical Eng. Dept. (2001-2005).
5. Graduate Coordinator (1997-2001).
Title: Designing, Manufacturing, and Clinical Feasibility Assessment of a Test Sample of Transcranial Direct & Alternative Current Stimulator Programmable with PC
Principal Investigator: Dr. Farzad Towhidkhah
Cooperators (in alphabetical order): Zeinab Esmailpoor, Amir Rajabi Moghadam, Fatemeh Yavari
Affiliation: Biomedical Engineering Department of Amirkabir University of Technology
Transcranial brain stimulation is one of the treatment methods that the results of its implementation in the treatment of different brain diseases or disorders such as depression, Parkinson, after stroke aphasia, neuralgia and addiction studies have been promising. In surface electrical stimulation of the brain, two (or more) electrodes placed on the scalp and a very weak (1-2 mA) constant (tDCS) or alternating (tACS) current is applied. It has been considered that this current stimulates the brain regions located in the path between the two electrodes. So far, no considerable complication has been reported after the correct usage of this method.
According to the positive effects of this therapeutic effect, now extensive researches are running to find more knowledge about of the mechanism of this method, increasing its efficiency and effectiveness as well as the possibility of its usage in the improvement of other neuro-cognitive diseases or disorders.
To extend the usage of this treatment method and its related research in Iran, in this project an example a transcranial direct/alternating current stimulator with the ability to produce variety shape of constant and alternating stimulation has been designed and implemented in the biomedical engineering department of Amirkabir University of technology (Figure (1)). This system can compete with the same foreign examples from the different aspects. The deigned device has the following features:
1. Ability to apply current from -4.5 to +4.5 mA
2. Current resolution = 25 ΅A; Frequency resolution = 1 Hz
3. The ability to apply a variety current waveforms, constant, sine, pulse, random or any arbitrary waveform designed.
4. Can be connected and programmed by computer to design new waveforms
5. Continuous monitoring and storage of the changes of the voltage and the impedance during stimulation.
6. Tickle mode to deal with the high impedance of the head at the beginning of the stimulation
7. Adjustable stimulation time duration
8. Touchscreen menus
9. Portable and user friendly
10. Long-lasting battery
11. Ability to select the Sham mode for research applicationsVarious technical assessments were done on the system to evaluate its safety as well as its sensitivity to the changes of the temperature and the load. During a clinical trial, the system was also examined in several modes, on 44 people in a motor activity. The result of this experiment was consistent with the observations of the previous studies. Based on the observations, some final modifications were done on the device. Now, this system is ready to offer to the clinics and research centers related to the field of neurocognitive studies and treatments.
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