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Updated: Oct 5, 2025

Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface

Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface

Published on: May 8, 2021

Deep learning for biosignal control: insights from basic to real-time methods with recommendations.

Arnau Dillen^1,2,3, Denis Steckelmacher², Kyriakos Efthymiadis²

¹Human Physiology and Sports Physiotherapy Research Group, Vrije Universiteit Brussel, Brussels, Belgium.

Journal of Neural Engineering

|January 27, 2022

Summary

This summary is machine-generated.

Deep learning (DL) enhances biosignal control for intuitive device interaction, particularly for individuals with paralysis. This review guides researchers in applying DL to real-world biosignal control systems, addressing current challenges and offering deployment strategies.

Keywords:

applied biosignal control deep learning human-computer interaction scoping review

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Area of Science:

Human-Computer Interaction
Biomedical Engineering
Artificial Intelligence

Background:

Biosignal control enables device interaction via biological signals, improving agency and accessibility for users with disabilities.
Current biosignal control methods face challenges in reliable intent decoding and user experience.
Deep learning (DL) offers potential for improved decoding accuracy but introduces computational complexity.

Purpose of the Study:

To introduce fundamental deep learning (DL) concepts for biosignal control.
To guide the deployment of DL-based real-time control systems for real-world applications.
To identify research gaps and challenges in DL for biosignal control.

Main Methods:

Scoping review of literature on biosignal control systems incorporating DL.
Focus on robotic device applications, the most active research area.
Analysis of implementation and evaluation strategies for DL-based control systems.

Main Results:

Key challenges in applying DL to biosignal control were identified.
Guidelines for designing, implementing, and evaluating DL-based biosignal control prototypes were formulated.
The review highlights the need for reliable intent decoding and improved user experience.

Conclusions:

This review provides a foundational understanding of DL for biosignal control.
It offers practical guidance for researchers new to the field.
It suggests future research directions for advancing DL in biosignal control systems.