Objective.Biosignal control is an interaction modality that allows users to interact with electronic devices by decoding the biological signals emanating from the movements or thoughts of the user. This manner of interaction with devices can enhance the sense of agency for users and enable persons suffering from a paralyzing condition to interact with everyday devices that would otherwise be challenging for them to use. It can also improve control of prosthetic devices and exoskeletons by making the interaction feel more natural and intuitive. However, with the current state of the art, several issues still need to be addressed to reliably decode user intent from biosignals and provide an improved user experience over other interaction modalities. One solution is to leverage advances in deep learning (DL) methods to provide more reliable decoding at the expense of added computational complexity. This scoping review introduces the basic concepts of DL and assists readers in deploying DL methods to a real-time control system that should operate under real-world conditions.Approach.The scope of this review covers any electronic device, but with an emphasis on robotic devices, as this is the most active area of research in biosignal control. We review the literature pertaining to the implementation and evaluation of control systems that incorporate DL to identify the main gaps and issues in the field, and formulate suggestions on how to mitigate them.Main results.The results highlight the main challenges in biosignal control with DL methods. Additionally, we were able to formulate guidelines on the best approach to designing, implementing and evaluating research prototypes that use DL in their biosignal control systems.Significance.This review should assist researchers that are new to the fields of biosignal control and DL in successfully deploying a full biosignal control system. Experts in their respective fields can use this article to identify possible avenues of research that would further advance the development of biosignal control with DL methods.
Keywords: applied; biosignal control; deep learning; human-computer interaction; scoping review.
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