DIRECTOR OF BCI LAB

DIRECTOR OF BCI LAB

Dr Tohid Yousefi Rezaii

Assistant Professor, Faculty of Electrical and Computer Engineering, Department of Biomedical Engineering

Dean of Biomedical Engineering Dapartment

University of Tabriz, Tabriz, Iran

 

Office:          ECE 215

Phone:         +98-41-3339-3748

Fax:              +98-41-3330-0819

Email:           yousefi@tabrizu.ac.ir

                      tohidyusefi@gmail.com

Homepage:  http://asatid.tabrizu.ac.ir/en/pages/default.aspx?yousefi

 

EDUCATIONAL BACKGROUND

Ph.D. - Electrical Engineering, Signal Processing Field, 2013

University of Tabriz, Tabriz, Iran, Thesis title: “Adaptive Recovery of time varying Sparse Signals in Compressive Sensing Domain”

M.Sc. - Electrical Engineering, Signal Processing Field, 2008

University of Tabriz, Tabriz, Iran, Thesis title: “Distributed multi-target tracking in wireless sensor networks using joint probabilistic data”

B.Sc. - Electrical Engineering, 2006

University of Tabriz, Tabriz, Iran, Thesis title: “Improved adaptive noise cancellation by active tap-weight detection”

HONORS

  • Highest GPA in Ph.D. program, Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran, 2013.
  • Highest GPA in M.Sc. program, Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran, 2008.
  • Highest GPA in B.Sc. program, Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran, 2006.
  • Being recognized as one of the exceptional talents by the office of Exceptional Talents at University of Tabriz, 2006-2013.
  • Selected as the first rank student in Ph.D. entrance exam in 2008.
  • Member of National Elite Organization of Iran.

RESEARCH INTERESTS     

  • Biomedical Signal Processing
  • Brain-Computer Interfacing
  • Compressed Sensing
  • Pattern Recognition and Statistical Signal Processing
  • Adaptive estimation
  • Sensor Networks and BioSensor Networks

 

COMPLETED RESEARCH PROJECTS     

  • Extracting and classifying cognitive features of Electroencephalography signal based on compressed sensing: application to HMI and mental disorder detection, supported by Cognitive Sciences and Technologies Council under grant number G10829.
  • Recognition of emotions provoked by auditory stimuli using EEG signal based on sparse representation-based classification

 

CURRENT RESEARCH PROJECTS

  • A driver assistant system to alert the driver fatigue based on EEG signal
  • Design and implementation of a prototype for braking intention detection from EEG signals
  • Brain visualization: multimodal learning approach for saliency detection with graph neural network
  • Attention deficit/hyperactivity disorder patterns diagnosis In children using event-related potential (ERP) source imaging of visual and auditory tasks
  • Classification of motor imagery EEG signals based on deep learning methods
  • Automatic sleep-stage classification based on information fusion theory
  • Prediction and detection of various epileptic seizures from single-channel EEG signal using sparse representation-based classification

 

PUBLICATIONS

  • Z. Mousavi, T. Yousefi Rezaii, S. Sheykhivand, A. Farzamnia, S. N. Razavi, “Deep convolutional neural network for classification of sleep stages from single-channel EEG signals,” to appear in The Journal of Neuroscience Methods, 2019.
  • M. Mehrkam, M. A. Tinati, T. Yousefi Rezaii, “Reconstruction of low-rank jointly sparse signals from multiple measurement vectors,” to appear in Signal, Image and Video Processing, 2019.
  • K. D. Ghanbar, T. Yousefi Rezaii, M. A. Tinati, A. Farzamnia, “Correlation-Based Regularized Common Spatial Patterns for Classification of Motor Imagery EEG Signals,” accepted to be presented in 27th Iranian Conference on Electrical Engineering (ICEE2019), 30 Apr. to 2 May, 2019.
  • V. Sharghian, T. Yousefi Rezaii, A. Farzamnia, M. A. Tinati, “Online Dictionary Learning for Sparse Representation-Based Classification of Motor Imagery EEG,” accepted to be presented in 27th Iranian Conference on Electrical Engineering (ICEE2019), 30 Apr. to 2 May, 2019.
  • M. Parvan, A. R. Ghiasi, T. Yousefi Rezaii, A. Farzamnia, “Transfer Learning based Motor Imagery EEG Signals Classification using Convolutional Neural Networks,” accepted to be presented in 27th Iranian Conference on Electrical Engineering (ICEE2019), 30 Apr. to 2 May, 2019.
  • G. Azarnia, M. A. Tinati, T. Yousefi Rezaii, “Generic cooperative and distributed algorithm for recovery of signals with the same sparsity profile in wireless sensor networks: a non-convex approach,” to appear in The Journal of Supercomputing, 2019.
  • A. A. Haghrah, M. A. Tinati, T. Yousefi Rezaii “Analysis of incremental LMS adaptive algorithm over wireless sensor networks with delayed-links,” Digital Signal Processing, vol. 88, pp. 80-89, 2019.
  • T. Yousefi Rezaii, S. Beheshti, M. Shamsi, S. Eftekharifar “ECG signal compression and denoising via optimum sparsity order selection in compressed sensing framework,” Biomedical Signal Processing and Control, vol. 41, pp. 161-171, 2018.
  • S. Eftekharifar, T. Yousefi Rezaii, S. Beheshti, S. Daneshvar, “Block sparse multi-lead ECG compression exploiting between-lead collaboration,” IET Signal Processing, vol. 13, no.  1, pp. 46-55, 2018.
  • M. Shamsi, T. Yousefi Rezaii, M. A. Tinati, A. Rastegarnia, A. Khalili, “Block Sparse Signal Recovery in Compressed Sensing: Optimum Active Block Selection and Within-Block Sparsity Order Estimation,” Circuits, Systems, and Signal Processing, vol. 37, no.  4, pp. 1649-1668, 2018.
  • S. Sheykhivand, T. Yousefi Rezaii, Z. Mousavi, S. Meshgini, “Automatic Stage Scoring of Single-Channel Sleep EEG using CEEMD of Genetic Algorithm and Neural Network,” Computational Intelligence in Electrical Engineering, vol. 9, no. 1, pp. 15-28, 2018.
  • S. Sheykhivand, T. Yousefi Rezaii, Z. Mousavi, S. Meshgini, “Automatic Stage Scoring of Single-Channel Sleep EEG using Discrete Wavelet Transform and a Hybrid Model of Simulated Annealing Algorithm and Neural Network,” Iranian Journal of Biomedical Engineering, vol. 11, no. 4, pp. 313-325, 2018.
  • R. Keshavarzian, A. Aghagolzadeh, T. Yousefi Rezaii, “Accelerated Proximal Gradient Method for Image Compressed Sensing Recovery Using Nonlocal Sparsity,” Iranian Conference on Electrical Engineering (ICEE), pp. 440-445, Iran, 2018.
  • M. R. G. Aghdam, T. Yousefi Rezaii, “An Adaptive Method for Under-Sampling of Mri Images Based On Compressive Sensing,” Processing vol. 3, no.  1, pp. 17-28, 2018.
  • S. Sheykhivand, T. Yousefi Rezaii, A. Farzamnia, M. Vazifehkhahi, “Sleep Stage Scoring of Single-Channel EEG Signal based on RUSBoost Classifier,” 2018 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET), Malaysia, 2018.
  • M. Abdollahpour, T. Yousefi Rezaii, A. Farzamnia, S. Meshgini, “Sleep Stage Classification Using Dempster-Shafer Theory for Classifier Fusion,” 2018 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET), Malaysia, 2018.
  • S. N. Adnani, M. A. Tinati, G. Azarnia, T. Yousefi Rezaii, “Energy-efficient data reconstruction algorithm for spatially-and temporally correlated data in wireless sensor networks,” IET Signal Processing, vol. 12, no. 8, pp. 1053-1062, 2018.
  • S. Eftekharifar, T. Yousefi Rezaii, S. Daneshvar, A. Rastegarnia, A. Khalili, “Multi-lead ECG Compression Based on Compressed Sensing Theory,” Computational Intelligence in Electrical Engineering, vol. 8, no. 2, pp. 13-24, 2017.
  • S. Sheykhivand, T. Yousefi Rezaii, A. N. Saatlo, N. Romooz, “Comparison Between Different Methods of Feature Extraction in BCI Systems Based on SSVEP,” International Journal of Industrial Mathematics, vol. 9, no. 4, pp. 341-347, 2017.
  • A. Khalili, A. Rastegarnia, M. K. Islam, T. Yousefi Rezaii, “Steady-state tracking analysis of adaptive filter with maximum correntropy criterion,” Circuits, Systems, and Signal Processing vol. 36, no. 4, pp. 1725-1734, 2017.
  • G. Azarnia, M. A. Tinati, T. Yousefi Rezaii, “Cooperative and distributed algorithm for compressed sensing recovery in WSNs” IET Signal Processing, vol. 12, no. 3, pp. 346-357, 2017.
  • A. A. Gharbali, J. M. Fonseca, S. Najdi, T. Yousefi Rezaii, “Automatic EOG and EMG Artifact Removal Method for Sleep Stage Classification,” Technological Innovation for Cyber-Physical Systems, 142-150, 2016.
  • S. Eftekharifar, T. Yousefi Rezaii, M. Shamsi, “A New Framework for ECG Signal Modeling and Compression Based on Compressed Sensing Theory,” 17th International Conference on Information, Communications and Signal Processing, 2015.
  • M. Shamsi, T. Yousefi Rezaii, S. Eftekharifar, “Sparsity Order Selection and Denoising in Compressed Sensing Framework,” 17th International Conference on Information, Communications and Signal Processing, 2015.
  • T. Yousefi Rezaii, S. Beheshti and M. A. Tinati, “Efficient LED-SAC Sparse Estimator Using Fast Sequential Adaptive Coordinate-Wise Optimization (LED-2SAC),” Hindawi Journal of Mathematical Problems in Engineering, pp. 1-8, vol. 2014, 2014.
  • T. Yousefi Rezaii, MA Tinati and S. Beheshti, “Sparsity aware consistent and high precision variable selection,” Springer Journal of Signal, Image and Video Processing, pp. 1613-1624, vol. 8, no. 8, 2014.
  • T. Yousefi Rezaii, M. A. Tinati, S. Beheshti, ” Adaptive efficient sparse estimator achieving oracle properties,” IET Signal Processing, pp. 259-268, vol. 7, no. 4, 2013.
  • T. Yousefi Rezaii, M. A. Tinati, “Adaptive Sparsity-aware parameter vector reconstruction with application to compressed sensing, Accepted to be included in proceedings of the International Conference on High Performance Computing and Simulation,” HPCS, July 4-8, Istanbul, Turkey, 2011.
  • T. Yousefi Rezaii, M. A. Tinati, “Distributed multi-target tracking using joint probabilistic data association and average consensus filter,” Annals of Telecommunications, pp. 1-14, Nov. 2010.
  • Tohid Yousefi Rezaii, Mohammad Ali Tinati, “Improved Joint Probabilistic Data Association Filter for Multi-Target Tracking in Wireless Sensor Networks,” Journal of Applied Science, 2008.
  • Tohid Yousefi Rezaii, Masoud Geravanchizadeh, “Transform Domain Based Multi-Channel noise cancellation based on SAD structure and LMMN Algorithm,” Journal of Applied Science, 2008.
  • Tohid Yousefi Rezaii, Masoud Geravanchizadeh, “Robust Multi-Channel Crosstalk Resistant Noise Cancellation Based on SAD Structure and LMMN Algorithm,” IEEE 3rd International Symposium on Communications, Control and Signal Processing, March 12-14 2008, Malta.
  • Mohammad Ali Tinati, Tohid Yousefi Rezaii, “Multi-Target tracking in Wireless Sensor Networks using Distributed Joint Probabilistic Data Association and Average Consensus Filter,” IEEE International Conference on Advanced Computer Control, Jan. 22-24 2009, Singapore.
  • Mohammad Ali Tinati, Tohid Yousefi Rezaii, “Regularized and Simplified Monte Carlo Joint Probabilistic Data Association Filter for Multi-Target Tracking in Wireless Sensor Networks,” The 9th IEEE International Symposium on Signal Processing and Information Technology, Dec. 14-17 2009, Ajman, UAE.
  • Mohammad Ali Tinati, Tohid Yousefi Rezaii, “Robust Nonlinear Echo Canceller with Adaptive Volterra Filter Applying VAD and Echo-Path delay Estimation,” IASTED International Conference on Communication Systems, Networks and Applications, Oct. 8-10, 2007, Beijing, China.
  • Mohammad Ali Tinati, Amir Rastegarnia, Tohid Yousefi Rezaii, “Receive Antenna Selection Algorithm to Improve Error Performance in MIMO Systems with Linear Receivers,” IASTED International Conference on Communication Systems, Networks and Applications, Oct. 8-10, 2007, Beijing, China.
  • Mohammad Ali Tinati, Amir Rastegarnia, Tohid Yousefi Rezaii, “A Transform Domain Based Least Mean Mixed Norm Algorithm to Improve Adaptive Beamforming,” IEEE 2nd International Conference on Electrical Engineering, March 25-26 2008, Lahore, Pakistan.

 

TEACHING EXPERIENCE

Graduate Level Courses:

Data Mining and Machine Learning, Functional Brain Imaging Systems, Advanced Biomedical Signal Processing, Estimation Theory, Digital Signal Processing, System Identification, Advanced Digital Signal Processing, Introduction to Compressed Sensing.

Undergraduate Level Courses:

Electric Circuits Theory, Probability and Random Variables, Logic Circuits, Communication Systems, Programming Language.

 

SKILLS

Hardware: Raspberry pi, Arduino, AVR Microcontroller, 21-channel Encephalan EEG recorder, OpenBCI CYTRON, 8DOF Robotic Arm

Language: English (fluent), Turkish (mother tongue), Persian (native), Arabic (basic)

Programming and Software: Python, Visual C, Pascal, EEG LAB, Matlab and Simulink