Search for: computer-interface
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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... 

    Detection of change to SSVEPs using analysis of phase space topological : a novel approach

    , Article Neurophysiology ; Volume 51, Issue 3 , 2019 , Pages 180-190 ; 00902977 (ISSN) Soroush, M. Z ; Maghooli, K ; Pisheh, N. F ; Mohammadi, M ; Soroush, P. Z ; Tahvilian, P ; Sharif University of Technology
    Springer New York LLC  2019
    A novel method based on EEG nonlinear analysis and analysis of steady-state visual evoked potentials (SSVEPs) has been processed. The EEG phase space is reconstructed, and some new geometrical features are extracted. Statistical analysis is carried out based on ANOVA, and most significant features are selected and then fed into a multi-class support vector machine (MSVM). Both offline and online phases are considered to fully address SSVEP detection. In the offline mode, the whole design evaluation, feature selection, and classifier training are performed. In the online scenario, the proposed method is evaluated and the detection rate is reported for both phases. Subject-dependent and... 

    A transfer learning algorithm based on linear regression for between-subject classification of EEG data

    , Article 25th International Computer Conference, Computer Society of Iran, CSICC 2020, 1 January 2020 through 2 January 2020 ; 2020 Samiee, N ; Sardouie, S. H ; Foroughmand Aarabi, M. H ; Sharif University of Technology
    Institute of Electrical and Electronics Engineers Inc  2020
    Classification is the most important part of brain-computer interface (BCI) systems. Because the neural activities of different individuals are not identical, using the ordinary methods of subject-dependent classification, does not lead to high accuracy in betweensubject classification problems. As a result, in this study, we propose a novel method for classification that performs well in between-subject classification. In the proposed method, at first, the subject-dependent classifiers obtained from the train subjects are applied to the test trials to obtain a set of scores and labels for the trials. Using these scores and the real labels of the labeled test trials, linear regression is... 

    Selection of efficient features for discrimination of hand movements from MEG using a BCI competition IV data set

    , Article Frontiers in Neuroscience ; Issue APR , 2012 ; 16624548 (ISSN) Sardouie, S. H ; Shamsollahi, M. B ; Sharif University of Technology
    The aim of a brain-computer interface (BCI) system is to establish a new communication system that translates human intentions, reflected by measures of brain signals such as magnetoencephalogram (MEG), into a control signal for an output device. In this paper, an algorithm is proposed for discriminating MEG signals, which were recorded during hand movements in four directions. These signals were presented as data set 3 of BCI competition IV. The proposed algorithm has four main stages: pre-processing, primary feature extraction, the selection of efficient features, and classification. The classification stage was a combination of linear SVM and linear discriminant analysis classifiers. The... 

    Efficient hardware implementation of real-time low-power movement intention detector system using fft and adaptive wavelet transform

    , Article IEEE Transactions on Biomedical Circuits and Systems ; Volume 11, Issue 3 , 2017 , Pages 585-596 ; 19324545 (ISSN) Chamanzar, A ; Shabany, M ; Malekmohammadi, A ; Mohammadinejad, S ; Sharif University of Technology
    Institute of Electrical and Electronics Engineers Inc  2017
    The brain-computer interfacing (BCI), a platform to extract features and classify different motor movement tasks from noisy and highly correlated electroencephalogram signals, is limited mostly by the complex and power-hungry algorithms. Among different techniques recently devised to tackle this issue, real-time onset detection, due to its negligible delay and minimal power overhead, is the most efficient one. Here, we propose a novel algorithm that outperforms the state-of-the-art design by sixfold in terms of speed, without sacrificing the accuracy for a real-time, hand movement intention detection based on the adaptive wavelet transform with only 1 s detection delay and maximum... 

    Development of a robust method for an online P300 Speller Brain Computer Interface

    , Article International IEEE/EMBS Conference on Neural Engineering, NER, San Diego, CA ; 2013 , Pages 1070-1075 ; 19483546 (ISSN); 9781467319690 (ISBN) Tahmasebzadeh, A ; Bahrani, M ; Setarehdan, S. K ; Sharif University of Technology
    This research presents a robust method for P300 component recognition and classification in EEG signals for a P300 Speller Brain-Computer Interface (BCI). The multiresolution wavelet decomposition technique was used for feature extraction. The feature selection was done using an improved t-test method. For feature classification the Quadratic Discriminant Analysis was employed. No any particular specification is previously assumed in the proposed algorithm and all the constants of the system are optimized to generate the highest accuracy on a validation set. The method is first verified in offline experiments on 'BCI competition 2003' data set IIb and data recorded by Emotiv Neuroheadset and... 

    Unsupervised cross-subject BCI learning and classification using riemannian geometry

    , Article 24th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2016, 27 April 2016 through 29 April 2016 ; 2016 , Pages 307-312 ; 9782875870278 (ISBN) Nasiri Ghosheh Bolagh, S ; Shamsollahi, M. B ; Jutten, C ; Congedo, M ; Sharif University of Technology
    i6doc.com publication  2016
    The inter-subject variability poses a challenge in cross-subject Brain-Computer Interface learning and classification. As a matter of fact, in cross-subject learning not all available subjects may improve the performance on a test subject. In order to address this problem we propose a subject selection algorithm and we investigate the use of this algorithm in the Riemannian geometry classification framework. We demonstrate that this new approach can significantly improve cross-subject learning without the need of any labeled data from test subjects  

    Robot control using an inexpensive P300 based BCI

    , Article 26th National and 4th International Iranian Conference on Biomedical Engineering, ICBME 2019, 27 November 2019 through 28 November 2019 ; 2019 , Pages 204-207 ; 9781728156637 (ISBN) Bahman, S ; Shamsollahi, M. B ; Sharif University of Technology
    Institute of Electrical and Electronics Engineers Inc  2019
    Brain Computer Interfaces (BCI) are an important concept of biomedical engineering because of their ability to improve life conditions for people with different disabilities. Lots of studies have worked on important characteristics of BCI systems, such as speed and accuracy, whereas price is an important aspect too. Thus, a low cost BCI system with high accuracy clearly can help more people. Our purpose in this study is to design a P300 based BCI system using a low-priced EEG headset which has an acceptable accuracy. Our final design got a mean real-time accuracy of 93.3% which is comparable to systems with much more expensive hardware. © 2019 IEEE  

    Variant combination of multiple classifiers methods for classifying the EEG signals in brain-computer interface

    , Article 13th International Computer Society of Iran Computer Conference on Advances in Computer Science and Engineering, CSICC 2008, Kish Island, 9 March 2008 through 11 March 2008 ; Volume 6 CCIS , 2008 , Pages 477-484 ; 18650929 (ISSN); 3540899847 (ISBN); 9783540899846 (ISBN) Shoaie Shirehjini, Z ; Bagheri Shouraki, S ; Esmailee, M ; Sharif University of Technology
    Controlling the environment with EEG signals is known as brain computer interface is the new subject researchers are interested in. The aim in such systems is to control the machine without using muscle, and we should control the machine using signals recorded from the surface of the cortex. In this project our focus is on pattern recognition phase in which we use multiple classifier fusion to improve the classification accuracy. We have applied various feature extraction methods and combined their results. Two methods, greedy algorithms and genetic algorithms, are used for selecting the pair feature extractor-classifier (we called expert) between the existed pair. Experiments show that with... 

    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%  

    Development of a MATLAB-based toolbox for brain computer interface applications in virtual reality

    , Article ICEE 2012 - 20th Iranian Conference on Electrical Engineering, 15 May 2012 through 17 May 2012 ; May , 2012 , Pages 1579-1583 ; 9781467311489 (ISBN) Afdideh, F ; Shamsollahi, M. B ; Resalat, S. N ; Sharif University of Technology
    Brain computer interface (BCI) is a widely used system to assist the disabled and paralyzed people by creating a new communication channel. Among the various methods used in BCI area, motor imagery (MI) is the most popular and the most common one due to its the most natural way of communication for the subject. Some software applications are used to implement BCI systems, and some toolboxes exist for EEG signal processing. In recent years virtual reality (VR) technology has entered into the BCI research area to simulate the real world situations and enhance the subject performance. In this work, a completely MATLAB-based MI-based BCI system is proposed and implemented in order to navigate... 

    High-speed SSVEP-based BCI: Study of various frequency pairs and inter-sources distances

    , Article Proceedings - IEEE-EMBS International Conference on Biomedical and Health Informatics: Global Grand Challenge of Health Informatics, BHI 2012 ; 2012 , Pages 220-223 ; 9781457721779 (ISBN) Resalat, S. N ; Saba, V ; Afdideh, F ; Heidarnejad, A ; Sharif University of Technology
    IEEE  2012
    Brain Computer Interface provides a new communication channel for people who have severe brain injuries. Among different types of BCIs, SSVEP-based one has been focused in recent years. In this type of BCI, selection of twinkling frequency of external visual stimulant and the distance between stimulants (in case of more than one stimulant) is so important. In this work, a SSVEP-based BCI with two external stimulants was designed. In order to determine the best twinkling frequency of stimulants and the best distance between them, the classification accuracy for seven different twinkling frequency pairs and five different stimulants distances was calculated. Two methods for feature extraction... 

    Five-class finger flexion classification using ECoG signals

    , Article 2010 International Conference on Intelligent and Advanced Systems, ICIAS 2010, 15 June 2010 through 17 June 2010 ; 2010 ; 9781424466238 (ISBN) Samiee, S ; Hajipour, S ; Shamsollahi, M. B ; Sharif University of Technology
    Increasing the number of car accidents and other cerebral disease cause to progress in using Brain-Compute Interface (BCI) as a common subject for research and treatment. The aim of Brain-Computer Interface system is to establish a new communication system that translates human intentions, reflected by brain signals, into a control signal for an output device such as a computer. To this end, different processes must be done on brain signals and these signals must be classified by suitable methods. There are various methods to classify ECoG signals which are different in features and classifiers. Used features depend on extracted features, feature reduction methods and measures of feature... 

    A novel dual and triple shifted RSVP paradigm for P300 speller

    , Article Journal of Neuroscience Methods ; Volume 328 , 2019 ; 01650270 (ISSN) Mijani, A. M ; Shamsollahi, M. B ; Sheikh Hassani, M ; Sharif University of Technology
    Elsevier B.V  2019
    Background: A speller system enables disabled people, specifically those with spinal cord injuries, to visually select and spell characters. A problem of primary speller systems is that they are gaze shift dependent. To overcome this problem, a single Rapid Serial Visual Presentation (RSVP) paradigm was initially introduced in which characters are displayed one-by-one at the center of a screen. New method: Two new protocols, Dual and Triple shifted RSVP paradigms, are introduced and compared against the single paradigm. In the Dual and Triple paradigms, two and three characters are displayed at the center of the screen simultaneously, holding the advantage of displaying the target character... 

    Extended common spatial and temporal pattern (ECSTP): A semi-blind approach to extract features in ERP detection

    , Article Pattern Recognition ; Volume 95 , 2019 , Pages 128-135 ; 00313203 (ISSN) Jalilpour Monesi, M ; Hajipour Sardouie, S ; Sharif University of Technology
    Elsevier Ltd  2019
    Common spatial pattern (CSP) analysis and its extensions have been widely used as feature extraction approaches in the brain-computer interfaces (BCIs). However, most of the CSP-based approaches do not use any prior knowledge that might be available about the two conditions (classes) to be classified. Therefore, their applications are limited to datasets that contain enough variance information about the two conditions. For example, in some event-related potential (ERP) detection applications, such as P300 speller, the information is in the time domain but not in the variance of spatial components. To address this problem, first, we present a novel feature extraction method termed extended... 

    A novel hybrid BCI speller based on RSVP and SSVEP paradigm

    , Article Computer Methods and Programs in Biomedicine ; Volume 187 , April , 2020 Jalilpour, S ; Hajipour Sardouie, S ; Mijani, A ; Sharif University of Technology
    Elsevier Ireland Ltd  2020
    Background and objective: Steady-state visual evoked potential (SSVEP) and rapid serial visual presentation (RSVP) are useful methods in the brain-computer interface (BCI) systems. Hybrid BCI systems that combine these two approaches can enhance the proficiency of the P300 spellers. Methods: In this study, a new hybrid RSVP/SSVEP BCI is proposed to increase the classification accuracy and information transfer rate (ITR) as compared with the other RSVP speller paradigms. In this paradigm, RSVP (eliciting a P300 response) and SSVEP stimulations are presented in such a way that the target group of characters is identified by RSVP stimuli, and the target character is recognized by SSVEP stimuli.... 

    RCTP: Regularized common tensor pattern for rapid serial visual presentation spellers

    , Article Biomedical Signal Processing and Control ; Volume 70 , September , 2021 ; 17468094 (ISSN) Jalilpour, S ; Hajipour Sardouie, S ; Sharif University of Technology
    Elsevier Ltd  2021
    Common Spatial Pattern (CSP) is a powerful feature extraction method in brain-computer interface (BCI) systems. However, the CSP method has some deficiencies that limit its beneficiary. First, this method is not useful when data is noisy, and it is necessary to have a large dataset because CSP is inclined to overfit. Second, the CSP method uses just the spatial information of the data, and it cannot incorporate the temporal and spectral information. In this paper, we propose a new CSP-based algorithm which is capable of employing the information in all dimensions of data. Also, by defining the regularization term for each mode of information, we can diminish the noise effects and overfitting... 

    Designing a Hybrid Brain Computer Interface System

    , M.Sc. Thesis Sharif University of Technology Mashayekh Bakhsh, Tara (Author) ; Shamsollahi, Mohammad Bagher (Supervisor)
    Brain Computer Interface (BCI) is a communication system between human brain and a computer or a peripheral device which by recording brain signals directly would send messages and commands from the human brain to computer.According to brain activity patterns of EEG, BCIs are divided into different types. The most important of these patterns called ERP (Event Related Potentials) which appears after particular events in the EEG signal. A significant ERP pattern is P300 potential. It occurs when patient recognizes oddball stimuli. SSVEP (Steady-State Visual Evoked Potential) is another type of patterns and is response of the brain to optical stimulations with certain frequencies and a strong... 

    The effect of constant and variable stimulus duration on p300 detection

    , Article 27th Iranian Conference on Electrical Engineering, ICEE 2019, 30 April 2019 through 2 May 2019 ; 2019 , Pages 1807-1811 ; 9781728115085 (ISBN) Jalilpour, S ; Hajipour Sardouie, S ; Mijani, A. M ; Sharif University of Technology
    Institute of Electrical and Electronics Engineers Inc  2019
    In this paper, a new stimulation protocol is proposed to detect the P300 component. In this protocol visual oddball paradigm is used to evoke Event Related Potentials (ERPs). Two types of stimulation protocol for P300 detection (the proposed and standard protocols) are compared in terms of the R-square coefficient and the amplitude of the P300 component. Statistical analysis (paired t-test) is applied to determine the significant differences between the two protocols. The proposed method can enhance the ability to detect the P300 component in comparison to the common protocol that has been provided so far (standard protocol)