Chay Machluf

ORCID: 0000-0003-0508-9908
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About
Contact & Profiles
Research Areas
  • EEG and Brain-Computer Interfaces
  • Auction Theory and Applications
  • Muscle activation and electromyography studies
  • Complexity and Algorithms in Graphs
  • Optimization and Search Problems
  • Motor Control and Adaptation

Tel Aviv University
2022-2023

Technion – Israel Institute of Technology
2017-2018

Purpose: A muscle synergies model was suggested to represent a simplifying motor control mechanism by the brainstem and spinal cord. The aim of study investigate feasibility such mechanisms in rehabilitation post-stroke individuals during execution hand-reaching movements multiple directions, compared non-stroke individuals. Methods: Twelve 13 participated study. Muscle were extracted from EMG data that recorded hand reaching tasks, using NMF algorithm. optimal number evaluated both groups...

10.3389/fncom.2018.00010 article EN cc-by Frontiers in Computational Neuroscience 2018-02-22

Functional tasks of the upper extremity can be executed by a variety muscular patterns, independent direction, speed and load task. This large number degrees freedom imposes significant control burden on CNS. Previous studies suggested that human cortex synchronizes discrete neural functional units within brainstem spinal cord, i.e. muscle synergies, linearly combining them to execute great repertoire movements. Further exploring this mechanism, we aim study whether single set synergies...

10.1109/tnsre.2017.2769659 article EN IEEE Transactions on Neural Systems and Rehabilitation Engineering 2017-11-02

Abstract Motivated by placement of jobs in physical machines, we introduce and analyze the problem online recoloring, or disengagement. In this problem, are given a set $n$ weighted vertices $k$-coloring (vertices represent jobs, colors machines). Edges, representing conflicts between inserted an fashion. After every edge insertion, algorithm must output proper vertices. The cost recoloring is sum weights whose color changed. Our aim to minimize competitive ratio algorithm, i.e., paid...

10.21203/rs.3.rs-1998221/v1 preprint EN cc-by Research Square (Research Square) 2022-08-30
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