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Computer Science

D-Index
45
Citations
7685
World Ranking
7252
National Ranking
290

Overview

Erik Scheme is affiliated with the University of New Brunswick in Canada. Their research primarily spans the fields of Engineering and Neuroscience, focusing on specialized areas such as Biomedical Engineering, Cognitive Neuroscience, Cellular and Molecular Neuroscience, Human-Computer Interaction, and Computer Vision and Pattern Recognition.

The scientist's work concentrates on several key topics, including:

  • Muscle activation and electromyography studies
  • EEG and Brain-Computer Interfaces
  • Neuroscience and Neural Engineering
  • Advanced Sensor and Energy Harvesting Materials
  • Hand Gesture Recognition Systems
  • Advanced Chemical Sensor Technologies
  • Diabetic Foot Ulcer Assessment and Management

Recent publications provide insight into the range of Scheme's research interests and contributions. Some notable papers include:

  • "Current Trends and Confounding Factors in Myoelectric Control: Limb Position and Contraction Intensity," 2020, Sensors
  • "Electromyography-Based Gesture Recognition: Is It Time to Change Focus From the Forearm to the Wrist?" 2020, IEEE Transactions on Industrial Informatics
  • "A Survey on Neuromarketing Using EEG Signals," 2021, IEEE Transactions on Cognitive and Developmental Systems
  • "Unsupervised Domain Adversarial Self-Calibration for Electromyography-Based Gesture Recognition," 2020, IEEE Access
  • "Fractal Analysis of Human Gait Variability via Stride Interval Time Series," 2020, Frontiers in Physiology

The main venues where Scheme frequently publishes include:

  • IEEE Transactions on Neural Systems and Rehabilitation Engineering
  • arXiv (Cornell University)
  • Journal of Neural Engineering
  • IEEE Access
  • Sensors

Collaboration is an important aspect of Scheme's research career. Frequent co-authors include Angkoon Phinyomark, Evan Campbell, Scott Bateman, Ethan Eddy, and Robyn Larracy, reflecting contributions across multiple projects and interdisciplinary studies.

Best Publications

  • Electromyogram pattern recognition for control of powered upper-limb prostheses: state of the art and challenges for clinical use.

    Erik Scheme;Kevin Englehart

  • Resolving the Limb Position Effect in Myoelectric Pattern Recognition

    A. Fougner;E. Scheme;A. D. C. Chan;K. Englehart

  • Feature Extraction and Selection for Myoelectric Control Based on Wearable EMG Sensors.

    Angkoon Phinyomark;Rami N. Khushaba;Erik J. Scheme

  • Multiple Binary Classifications via Linear Discriminant Analysis for Improved Controllability of a Powered Prosthesis

    L.J. Hargrove;E.J. Scheme;K.B. Englehart;B.S. Hudgins

  • EMG Pattern Recognition in the Era of Big Data and Deep Learning

    Angkoon Phinyomark;Erik J. Scheme

  • Proceedings of the first workshop on Peripheral Machine Interfaces: going beyond traditional surface electromyography

    Claudio Castellini;Panagiotis K. Artemiadis;Michael Wininger;Arash Ajoudani

  • Selective Classification for Improved Robustness of Myoelectric Control Under Nonideal Conditions

    E J Scheme;K B Englehart;B S Hudgins

  • Support Vector Regression for Improved Real-Time, Simultaneous Myoelectric Control

    Ali Ameri;Ernest N. Kamavuako;Erik J. Scheme;Kevin B. Englehart

  • Examining the adverse effects of limb position on pattern recognition based myoelectric control

    E. Scheme;A. Fougner;O. Stavdahl;A.D.C. Chan

  • Interpreting Deep Learning Features for Myoelectric Control: A Comparison With Handcrafted Features.

    Ulysse Côté-Allard;Evan Campbell;Angkoon Phinyomark;François Laviolette

  • Regression convolutional neural network for improved simultaneous EMG control.

    Ali Ameri;Mohammad Ali Akhaee;Erik Scheme;Kevin Englehart

  • A Deep Transfer Learning Approach to Reducing the Effect of Electrode Shift in EMG Pattern Recognition-Based Control

    Ali Ameri;Mohammad Ali Akhaee;Erik Scheme;Kevin Englehart

  • High-density force myography: A possible alternative for upper-limb prosthetic control.

    Ashkan Radmand;Erik Scheme;Kevin Englehart

  • Confidence-Based Rejection for Improved Pattern Recognition Myoelectric Control

    E. J. Scheme;B. S. Hudgins;K. B. Englehart

  • Motion Normalized Proportional Control for Improved Pattern Recognition-Based Myoelectric Control

    Erik Scheme;Blair Lock;Levi Hargrove;Wendy Hill

  • Current Trends and Confounding Factors in Myoelectric Control: Limb Position and Contraction Intensity.

    Evan Campbell;Angkoon Phinyomark;Erik J. Scheme

  • Electromyography-Based Gesture Recognition: Is It Time to Change Focus From the Forearm to the Wrist?

    Fady S. Botros;Angkoon Phinyomark;Erik J. Scheme

  • Validation of a Selective Ensemble-Based Classification Scheme for Myoelectric Control Using a Three-Dimensional Fitts' Law Test

    Erik J. Scheme;Kevin B. Englehart

  • Training Strategies for Mitigating the Effect of Proportional Control on Classification in Pattern Recognition Based Myoelectric Control.

    Erik Scheme;Kevin Englehart

  • Real-Time, Simultaneous Myoelectric Control Using Force and Position-Based Training Paradigms

    Ali Ameri;Erik J. Scheme;Ernest Nlandu Kamavuako;Kevin B. Englehart

  • Continuous Detection and Decoding of Dexterous Finger Flexions With Implantable MyoElectric Sensors

    J J Baker;E Scheme;K Englehart;D T Hutchinson

Frequent Co-Authors

Kevin Englehart
Kevin Englehart University of New Brunswick
Angkoon Phinyomark
Angkoon Phinyomark University of New Brunswick
Levi J. Hargrove
Levi J. Hargrove Northwestern University
Pradeep Kumar
Pradeep Kumar Indian Institute of Technology Roorkee
François Laviolette
François Laviolette Université Laval
Philip A. Parker
Philip A. Parker University of New Brunswick
Partha Pratim Roy
Partha Pratim Roy Indian Institute of Technology Dhanbad
Antonio Bicchi
Antonio Bicchi Italian Institute of Technology
Christian Cipriani
Christian Cipriani Sant'Anna School of Advanced Studies
Debi Prosad Dogra
Debi Prosad Dogra Indian Institute of Technology Bhubaneswar

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