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    A meshless EFG-based algorithm for 3D deformable modeling of soft tissue in real-time

    , Article Studies in Health Technology and Informatics, 9 February 2012 through 11 February 2012 ; Volume 173 , February , 2012 , Pages 1-7 ; 09269630 (ISSN) ; 9781614990215 (ISBN) Abdi, E ; Farahmand, F ; Durali, M ; Sharif University of Technology
    The meshless element-free Galerkin method was generalized and an algorithm was developed for 3D dynamic modeling of deformable bodies in real time. The efficacy of the algorithm was investigated in a 3D linear viscoelastic model of human spleen subjected to a time-varying compressive force exerted by a surgical grasper. The model remained stable in spite of the considerably large deformations occurred. There was a good agreement between the results and those of an equivalent finite element model. The computational cost, however, was much lower, enabling the proposed algorithm to be effectively used in real-time applications  

    MEG based classification of wrist movement

    , Article Proceedings of the 31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Engineering the Future of Biomedicine, EMBC 2009 ; 2009 , Pages 986-989 ; 1557170X (ISSN) ; 978-142443296-7 (ISBN) Montazeri, N ; Shamsollahi, M. B ; Hajipour, S ; Sharif University of Technology
    Neural activity is very important source for data mining and can be used as a control signal for brain-computer interfaces (BCIs). Particularly, Magnetic signals of neurons are enriched with information about the movement of different part of the body such as wrist movement. In this paper, we use MEG (Magneto encephalography) signals of two subjects recorded during wrist movement task in four directions. Data were prepared for BCI competition 2008 for multiclass classification. Our approach for this classification problem consists of PCA as a noise reduction method, ULDA for feature reduction and various linear classifiers such as Bayesian, KNN and SVM. Final results (58%-62% for subject 1... 

    Tool-tissue force estimation in laparoscopic surgery using geometric features

    , Article Studies in Health Technology and Informatics ; Volume 184 , 2013 , Pages 225-229 ; 09269630 (ISSN) Kohani, M ; Behzadipour, S ; Farahmand, F ; Sharif University of Technology
    IOS Press  2013
    This paper introduces three geometric features, from deformed shape of a soft tissue, which demonstrate good correlation with probing force and maximum local stress. Using FEM simulation, 2D and 3D model of an in vivo porcine liver was built for different probing tasks. Maximum deformation angle, maximum deformation depth and width of displacement constraint of the reconstructed shape of the deformed body were calculated. Two neural networks were trained from these features and the calculated interaction forces. The features are shown to have high potential to provide force estimation either for haptic devices or to assess the damage to the tissue in large deformations of up to 40%  

    Real-time simulation of the nonlinear visco-elastic deformations of soft tissues

    , Article International Journal of Computer Assisted Radiology and Surgery ; Volume 6, Issue 3 , 2011 , Pages 297-307 ; 18616410 (ISSN) Basafa, E ; Farahmand, F ; Sharif University of Technology
    Purpose: Mass-spring-damper (MSD) models are often used for real-time surgery simulation due to their fast response and fairly realistic deformation replication. An improved real time simulation model of soft tissue deformation due to a laparoscopic surgical indenter was developed and tested. Method: The mechanical realization of conventional MSD models was improved using nonlinear springs and nodal dampers, while their high computational efficiency was maintained using an adapted implicit integration algorithm. New practical algorithms for model parameter tuning, collision detection, and simulation were incorporated. Results: The model was able to replicate complex biological soft tissue... 

    A validation study of a virtual-based haptic system for endoscopic sinus surgery training

    , Article International Journal of Medical Robotics and Computer Assisted Surgery ; Volume 15, Issue 6 , 2019 ; 14785951 (ISSN) Sadeghnejad, S ; Khadivar, F ; Abdollahi, E ; Moradi, H ; Farahmand, F ; Sadr Hosseini, S. M ; Vossoughi, G ; Sharif University of Technology
    John Wiley and Sons Ltd  2019
    Background: The development of endoscopic sinus surgery (ESS) training simulators for clinical environment applications has reduced the existing shortcomings in conventional teaching methods, creating a standard environment for trainers and trainees in a more accurate and repeatable fashion. Materials and methods: In this research, the validation study of an ESS training simulator has been addressed. It is important to consider components that guide trainees to improve their hand movements control in the orbital floor removal in an ESS operation. Therefore, we defined three tasks to perform: pre-experiment learning, training, and evaluation. In these tasks, the critical regions introduced in... 

    Assessment of preprocessing on classifiers used in the P300 speller paradigm

    , Article 28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'06, New York, NY, 30 August 2006 through 3 September 2006 ; 2006 , Pages 1319-1322 ; 05891019 (ISSN); 1424400325 (ISBN); 9781424400324 (ISBN) Mirghasemi, H ; Shamsollahi, M. B ; Fazel Rezai, R ; Sharif University of Technology
    Artifact removal is an essential part in electroencephalogram (EEG) recording and the raw EEG signals require preprocessing before feature extraction. In this work, we implemented three filtering methods and demonstrated their effects on the performance of different classifiers. Bandpass digital filtering, median filtering and facet method are three preprocessing approaches investigated in this paper. We used data set lib from the BCI competition 2003 for training and testing phase. Our accuracy varied between 80% and 96%. In our work, we demonstrated that the problems of choosing the classifier and preprocessing methods are not independent of each other. Two of our approaches could achieve... 

    Analysis of P300 classifiers in brain computer interface speller

    , Article 28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'06, New York, NY, 30 August 2006 through 3 September 2006 ; 2006 , Pages 6205-6208 ; 05891019 (ISSN); 1424400325 (ISBN); 9781424400324 (ISBN) Mirghasemi, H ; Fazel Rezai, R ; Shamsollahi, M. B ; Sharif University of Technology
    In this paper, the performance of five classifiers in P300 speller paradigm are compared. Theses classifiers are Linear Support Vector Machine (LSVM), Gaussian Support Vector Machine (RSVM), Neural Network (NN), Fisher Linear Discriminant (FLD), and Kernel Fisher Discriminant (KFD). In classification of P300 waves, there has been a trend to use SVM classifiers. Although they have shown a good performance, in this paper, it is shown that the FLD classifiers outperform the SVM classifiers. FLD classifier uses only ten channels of the recorded electroencephalogram (EEG) signals. This makes them a very good candidate for real-time applications. In addition, FLD approach does not need any...