Name
Title | ||
|---|---|---|
Linear vs Non-Linear Mapping in a Body Machine Interface Based on Electromyographic Signals |
Abstract | ||
|---|---|---|
The human machine interface (HMI) refers to a paradigm in which the users interact with external devices through an interface that mediates the information exchanges between them and the device. In this work we focused on a HMI that exploits signals derived from the body to control the machine: the body machine interface (BMI). It is reasonable to assume that signals derived from body movements, electromyography activity, as well as brain activity, have a non-linear nature. This implies that linear algorithms cannot exploit all the information contained in these signals. In this work we proposed a new BMI that maps electromyographic signals into the control of a computer cursor by using a new non-linear dimensionality reduction algorithm based on autoassociative neural network. We tested the system on a group of ten healthy subjects that, controlling this cursor, performed a reaching task. We compared the result with the performance of an age and gender matched group of healthy subjects that solved the same task using a BMI based on a linear mapping. The analysis of the performance indices showed a substantial difference between the two groups. In particular, the performance of the people using the non-linear mapping were better in terms of time, accuracy and smoothness of the cursor's movement. This study opened the way to the exploitation of non-linear dimensionality reduction algorithms to pursue a new and effective clinical approach for body-machine interfaces. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/BIOROB.2018.8487185 | 2018 7th IEEE International Conference on Biomedical Robotics and Biomechatronics (Biorob) |
Keywords | Field | DocType |
maps electromyographic signals,linear algorithms,nonlinear nature,body movements,HMI,human machine interface,body machine interface,body-machine interfaces,nonlinear dimensionality reduction algorithm,nonlinear mapping,linear mapping,BMI | Dimensionality reduction,Nonlinear system,Pattern recognition,Computer science,Brain activity and meditation,Feature extraction,Linear map,Artificial intelligence,Artificial neural network,Smoothness,Principal component analysis | Conference |
ISSN | ISBN | Citations |
2155-1774 | 978-1-5386-8184-8 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Camilla Pierella | 1 | 6 | 1.73 |
| A. Sciacchitano | 2 | 0 | 0.34 |
| Ali Farshchiansadegh | 3 | 6 | 1.73 |
| Maura Casadio | 4 | 22 | 10.03 |
| Ferdinando A. Mussa-Ivaldi | 5 | 239 | 73.78 |