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Gideon F. Inbar
Researcher at Technion – Israel Institute of Technology
Publications - 88
Citations - 2702
Gideon F. Inbar is an academic researcher from Technion – Israel Institute of Technology. The author has contributed to research in topics: Adaptive control & Muscle spindle. The author has an hindex of 27, co-authored 88 publications receiving 2661 citations. Previous affiliations of Gideon F. Inbar include University of California, Davis & University of Amsterdam.
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An improved P300-based brain-computer interface
TL;DR: The presented BCI achieves excellent performance compared to other existing BCIs, and allows a reasonable communication rate, while maintaining a low error rate.
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Classification of finger activation for use in a robotic prosthesis arm
TL;DR: This paper proposes using the electromyographic signals recorded by two pairs of electrodes placed over the arm for operating a robotic prosthesis using multiple features from these signals extracted whence the most relevant features are selected by a genetic algorithm as inputs for a simple classifier.
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Autoregressive Modeling of Surface EMG and Its Spectrum with Application to Fatigue
Omry Paiss,Gideon F. Inbar +1 more
TL;DR: An investigation of the ability of the autoregressive (AR) model to describe the spectrum of the processes underlying the recorded surface EMG, carrying out SEMG measurements on the biceps brachii muscle with fixed surface electrodes arrangement and isotonic conditions.
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Feature selection for the classification of movements from single movement-related potentials
Elad Yom-Tov,Gideon F. Inbar +1 more
TL;DR: It is shown that it is possible to differentiate between the movements of two limbs with a classification accuracy of 87% using as little as 10 features without subject training, and a 63% classification accuracy rate can be reached when attempting to distinguish between three limbs.
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On Surface EMG Spectral Characterization and Its Application to Diagnostic Classification
TL;DR: Surface EMG was recorded from the biceps with fixed muscle length at S0 percent maximal voluntary contraction and the signal bandpass was 10-230 Hz where most of the surface EMG energy is located.