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Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding

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Published on: July 26, 2013

A multi-Kalman filter-based approach for decoding arm kinematics from EMG recordings.

Hend ElMohandes^1,2, Seif Eldawlatly^3,4, Josep Marcel Cardona Audí⁵

¹Center of Informatics Science, Nile University, Giza, Egypt.

Biomedical Engineering Online

|September 3, 2022

Summary

This study introduces a novel multi-Kalman filter approach for decoding arm kinematics from Electromyography (EMG) signals, enabling more natural prosthetic arm control. The method shows promise for reliable, continuous, and simultaneous movement decoding, potentially leading to subject-independent prosthetic systems.

Keywords:

Decoding EMG Kalman filter Prosthetic arms

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Published on: May 26, 2020

Area of Science:

Biomedical Engineering
Rehabilitation Robotics
Signal Processing

Background:

Advancements in myoelectric prosthetics face challenges in reliability, naturalistic control, and computational demands.
Existing systems often lack simultaneous, continuous control and are limited in movement scope.

Purpose of the Study:

To propose and evaluate an Electromyography (EMG)-based multi-Kalman filter approach for decoding continuous and simultaneous arm kinematics.
To decode elbow angle (θ) and wrist joint positions (X, Y) for enhanced prosthetic arm functionality.

Main Methods:

Recorded arm kinematics and EMG signals from biceps, triceps, and deltoid muscles of ten subjects.
Developed and applied a multi-Kalman filter algorithm to decode kinematic data from EMG signals.
Assessed decoder performance using correlation coefficient (CC) and normalized root-mean-square error (NRMSE).

Main Results:

Within-subject decoding achieved average CC of 0.68 (θ), 0.67 (X), and 0.64 (Y), with NRMSE of 0.21, 0.18, and 0.24, respectively.
Cross-subject decoding yielded average CC of 0.61 (θ), 0.61 (X), and 0.48 (Y), with NRMSE of 0.23, 0.20, and 0.38, respectively.

Conclusions:

The proposed EMG-based multi-Kalman filter approach demonstrates efficacy in decoding arm kinematics.
Results suggest the potential for developing a subject-independent decoder for more accessible myoelectric prostheses.