4.7 Review

Interpreting Volitional Movement Intent From Biological Signals: A Review

期刊

IEEE SIGNAL PROCESSING MAGAZINE
卷 38, 期 4, 页码 23-33

出版社

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/MSP.2021.3074778

关键词

Biomedical signal processing; Machine learning algorithms; Training data; Signal processing algorithms; Machine learning; Biology; Decoding

资金

  1. National Science Foundation [1533649, 1901492, 1901236]
  2. DARPA's HAPTIX program [N66001-15-C-4017]
  3. Direct For Computer & Info Scie & Enginr
  4. Div Of Information & Intelligent Systems [1901236] Funding Source: National Science Foundation
  5. Direct For Computer & Info Scie & Enginr
  6. Div Of Information & Intelligent Systems [1901492] Funding Source: National Science Foundation
  7. Division Of Behavioral and Cognitive Sci
  8. Direct For Social, Behav & Economic Scie [1533649] Funding Source: National Science Foundation

向作者/读者索取更多资源

This article reviews technologies and algorithms for decoding volitional movement intent using bioelectrical signals recorded from the human body, addressing deficiencies in current state-of-the-art methods and proposing three approaches to mitigate them. Experimental results demonstrate the effectiveness of these approaches.
This article reviews technologies and algorithms for decoding volitional movement intent using bioelectrical signals recorded from the human body. Such signals include electromyograms, electroencephalograms, electrocorticograms, intracortical recordings, and electroneurograms. After reviewing signal features commonly used for interpreting movement intent, this article describes traditional movement decoders based on Kalman filters (KFs) and machine learning (ML). A number of deficiencies of the current state of the art in this field are described, and three approaches that mitigate some of these deficiencies are reviewed. They include data aggregation-based training to improve decoder performance when only limited amounts of training data are available, a shared controller that incorporates estimates of movement goals, and an adaptive decoder designed to compensate for time variations in the relationships between the human body and the prosthesis. Also included are experimental results that illustrate some of the concepts discussed in the article.

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