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

Abstract

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. 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. Additionally, we formulate guidelines on the best approach to designing, implementing and evaluating research prototypes that use DL in their biosignal control systems.

Additive manufacturing systems integration

“…This is a place for efficient information management [41], [42], cross-functional focus, creative learning [43], [44], and data management for the industries' [45] factory-level constants. Remote operations success requires adequate training against whirring intelligent surfaces to enhance capabilities and domain knowledge accessibility [46]. Real-world examples of virtual operations involve multi-agent model immersion across various industries [47].…”

On the Influence of Aging on Classification Performance in the Visual EEG Oddball Paradigm Using Statistical and Temporal Features

“…Especially LDA [ 28 , 29 , 30 ] and SVM [ 31 , 32 , 33 ] have been highly used in practice, but also KNN [ 34 ], LR [ 35 ] and RF [ 36 ]. Despite the current high interest in deep neural network (DNN) architectures (see e.g., [ 6 , 37 , 38 , 39 , 40 , 41 , 42 ]), these were not considered here due to the limited training data available and the number of hyperparameters that would need to be defined.…”

Using Deep Learning Models to Predict Prosthetic Ankle Torque

“…The field of wearable robotics is increasingly incorporating deep learning to improve control systems and user adaptability [ 24 ]. Recent studies have applied deep learning for intuitive control of powered knee–ankle prostheses [ 25 ], estimating joint moments and ground reaction forces in various walking conditions [ 26 ], and predicting joint moments in real time for exoskeleton controllers [ 27 , 28 ].…”