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Engineering Platform and Experimental Protocol for Design and Evaluation of a Neurally-controlled Powered Transfemoral Prosthesis

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Published on: July 22, 2014

Hybrid Functional Near-Infrared Spectroscopy System and Electromyography for Prosthetic Knee Control.

Nouf Jubran AlQahtani¹, Ibraheem Al-Naib^2,3, Ijlal Shahrukh Ateeq¹

¹Biomedical Engineering Department, College of Engineering, Imam Abdulrahman Bin Faisal University, Dammam 34212, Saudi Arabia.

|November 26, 2024

Summary

This summary is machine-generated.

This study combined electromyography (EMG) and functional near-infrared spectroscopy (fNIRS) to improve prosthetic knee control using motor imagery. The hybrid system enhanced classification accuracy, paving the way for advanced prosthetic limb technology.

Keywords:

brain–computer interfaces (BCIs)electromyography (EMG)functional near-infrared spectroscopy (fNIRS)lower prosthetic limbs motor imagery (MI)neurorehabilitation

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A Structured Rehabilitation Protocol for Improved Multifunctional Prosthetic Control: A Case Study

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Functional Near Infrared Spectroscopy of the Sensory and Motor Brain Regions with Simultaneous Kinematic and EMG Monitoring During Motor Tasks

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Published on: December 5, 2014

A Structured Rehabilitation Protocol for Improved Multifunctional Prosthetic Control: A Case Study

A Structured Rehabilitation Protocol for Improved Multifunctional Prosthetic Control: A Case Study

Published on: November 6, 2015

Area of Science:

Biomedical Engineering
Neuroscience
Rehabilitation Technology

Background:

Growing limb loss necessitates advanced prosthetic solutions.
Brain-computer interfaces (BCIs) offer enhanced control for prosthetics.
Motor imagery combined with biosignal processing shows promise for intuitive prosthetic control.

Purpose of the Study:

To develop and validate a hybrid BCI system integrating electromyography (EMG) and functional near-infrared spectroscopy (fNIRS).
To enhance the control accuracy of prosthetic knee movements for individuals with above-knee amputations using motor imagery.

Main Methods:

A hybrid system was developed using EMG to capture muscular activity and fNIRS to monitor hemodynamic brain responses.
Nine healthy male participants performed real and imagined knee movements (motor imagery).
Data from OpenBCI Cyton board (EMG) and fNIRS were analyzed using Support Vector Machine and Linear Discriminant Analysis.

Main Results:

The hybrid EMG and fNIRS system significantly improved classification accuracy compared to individual modalities.
Support Vector Machine achieved 49.61% accuracy for motor imagery tasks.
Linear Discriminant Analysis achieved 89.67% accuracy for real execution tasks.

Conclusions:

A hybrid EMG and fNIRS approach is feasible for prosthetic knee control via motor imagery.
This integrated system represents a significant advancement in prosthetic technology.
The findings support the potential for more intuitive and responsive prosthetic limbs.