Search for: motor-cortex
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    Decoding Hand Trajectory from Primary Motor Cortex ECoG Using Time Delay Neural Network

    , Article Communications in Computer and Information Science ; Vol. 459 CCIS, issue , September , 2014 , p. 237-247 Kifouche, A ; Vigneron, V ; Shamsollahi, M. B ; Guessoum, A ; Sharif University of Technology
    Brain-machines - also termed neural prostheses, could potentially increase substantially the quality of life for people suffering from motor disorders or even brain palsy. In this paper we investigate the non-stationary continuous decoding problem associated to the rat's hand position. To this aim, intracortical data (also named ECoG for electrocorticogram) are processed in successive stages: spike detection, spike sorting, and intention extraction from the firing rate signal. The two important questions to answer in our experiment are (i) is it realistic to link time events from the primary motor cortex with some time-delay mapping tool and are some inputs more suitable for this mapping... 

    Stroke Neuromusculoskeletal Modeling in Order to Understand Rehabilitative Interventions

    , Ph.D. Dissertation Sharif University of Technology Hajihosseinali, Majid (Author) ; Behzadipour, Saeed (Supervisor) ; Farahmand, Farzam (Supervisor)
    This dissertation focuses on modeling human hand movements post-stroke to understand rehabilitative interventions. The model consists of a multi-layer neural network as the motor cortex in the brain and a two-degree freedom biomechanical model with six muscles for the simulation of hand movement in the horizontal plane. A passive robotic device has been designed and manufactured to record planar hand movements. Movements during reaching tasks have been recorded in 24 chronic stroke patients, as well as 18 healthy subjects, using the robotic device. Statistical analysis of the data showed that movements in a particular direction (NW-SE) are more sensitive to stroke, improve after... 

    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... 

    Transcranial DC stimulation modifies functional connectivity of large-scale brain networks in abstinent methamphetamine users

    , Article Brain and Behavior ; Volume 8, Issue 3 , 2018 ; 21623279 (ISSN) Shahbabaie, A ; Ebrahimpoor, M ; Hariri, A ; Nitsche, M. A ; Hatami, J ; Fatemizadeh, E ; Oghabian, M. A ; Ekhtiari, H ; Sharif University of Technology
    John Wiley and Sons Ltd  2018
    Background: Transcranial direct current stimulation (tDCS) is a noninvasive brain stimulation tool suited to alter cortical excitability and activity via the application of weak direct electrical currents. An increasing number of studies in the addiction literature suggests that tDCS modulates subjective self-reported craving through stimulation of dorsolateral prefrontal cortex (DLPFC). The major goal of this study was to explore effects of bilateral DLPFC stimulation on resting state networks (RSNs) in association with drug craving modulation. We targeted three large-scale RSNs; the default mode network (DMN), the executive control network (ECN), and the salience network (SN). Methods:... 

    The 2017 and 2018 Iranian Brain-Computer interface competitions

    , Article Journal of Medical Signals and Sensors ; Volume 10, Issue 3 , 2020 , Pages 208-216 Aghdam, N ; Moradi, M ; Shamsollahi, M ; Nasrabadi, A ; Setarehdan, S ; Shalchyan, V ; Faradji, F ; Makkiabadi, B ; Sharif University of Technology
    Isfahan University of Medical Sciences(IUMS)  2020
    This article summarizes the first and second Iranian brain-computer interface competitions held in 2017 and 2018 by the National Brain Mapping Lab. Two 64-channel electroencephalography (EEG) datasets were contributed, including motor imagery as well as motor execution by three limbs. The competitors were asked to classify the type of motor imagination or execution based on EEG signals in the first competition and the type of executed motion as well as the movement onset in the second competition. Here, we provide an overview of the datasets, the tasks, the evaluation criteria, and the methods proposed by the top-ranked teams. We also report the results achieved with the submitted algorithms... 

    Multiclass classification of patients during different stages of Alzheimer's disease using fMRI time-series

    , Article Biomedical Physics and Engineering Express ; Volume 6, Issue 5 , 2020 Ahmadi, H ; Fatemizadeh, E ; Motie Nasrabadi, A ; Sharif University of Technology
    IOP Publishing Ltd  2020
    Alzheimer's Disease (AD) begins several years before the symptoms develop. It starts with Mild Cognitive Impairment (MCI) which can be separated into Early MCI and Late MCI (EMCI and LMCI). Functional connectivity analysis and classification are done among the different stages of illness with Functional Magnetic Resonance Imaging (fMRI). In this study, in addition to the four stages including healthy, EMCI, LMCI, and AD, the patients have been tracked for a year. Indeed, the classification has been done among 7 groups to analyze the functional connectivity changes in one year in different stages. After generating the functional connectivity graphs for eliminating the weak links, three... 

    Real-time intelligent pattern recognition algorithm for surface EMG signals

    , Article BioMedical Engineering Online ; Volume 6 , 3 December , 2007 ; 1475925X (ISSN) Khezri, M ; Jahed, M ; Sharif University of Technology
    Background: Electromyography (EMG) is the study of muscle function through the inquiry of electrical signals that the muscles emanate. EMG signals collected from the surface of the skin (Surface Electromyogram: sEMG) can be used in different applications such as recognizing musculoskeletal neural based patterns intercepted for hand prosthesis movements. Current systems designed for controlling the prosthetic hands either have limited functions or can only be used to perform simple movements or use excessive amount of electrodes in order to achieve acceptable results. In an attempt to overcome these problems we have proposed an intelligent system to recognize hand movements and have provided...