Interpreting Volitional Movement Intent From Biological Signals: A Review

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Interpreting Volitional Movement Intent From Biological Signals: A Review

By: 
Henrique Dantas; Taylor C. Hansen; David J. Warren; V. John Mathews

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.

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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