In this study, we consider a non-invasive body-machine interface that captures body motions still available to people with spinal cord injury (SCI) and maps them into a set of signals for controlling a computer user interface while engaging in a sustained level of mobility and exercise. We compare the effectiveness of two decoding algorithms that transform a high-dimensional body-signal vector into a lower dimensional control vector on six subjects with high-level SCI and eight controls. One algorithm is based on a static map from current body signals to the current value of the control vector set through principal component analysis (PCA), the other on dynamic mapping a segment of body signals to the value and the temporal derivatives of the control vector set through a Kalman filter. SCI and control participants performed straighter and smoother cursor movements with the Kalman algorithm during center-out reaching, but their movements were faster and more precise when using PCA. All participants were able to use the BMI's continuous, two-dimensional control to type on a virtual keyboard and play pong, and performance with both algorithms was comparable. However, seven of eight control participants preferred PCA as their method of virtual wheelchair control. The unsupervised PCA algorithm was easier to train and seemed sufficient to achieve a higher degree of learnability and perceived ease of use.
|Titolo:||Static Versus Dynamic Decoding Algorithms in a Non-Invasive Body-Machine Interface|
|Data di pubblicazione:||2017|
|Appare nelle tipologie:||01.01 - Articolo su rivista|
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|nihms-844192.pdf||Author manuscript IEEE Trans Neural Syst Rehabil Eng. Author manuscript; available in PMC 2017 August 09- Published in final edited form as: IEEE Trans Neural Syst Rehabil Eng. 2017 July ; 25(7): 893–905. doi:10.1109/TNSRE.2016.2640360||Documento in Post-print||Open Access Visualizza/Apri|