5 IEEE SIGNAL PROCESSING MAGAZINE | July 2021 | FROM THE GUEST EDITORS Dario Farina, Arash Mohammadi, Tülay Adali, Nitish V. Thakor, and Konstantinos N. Plataniotis D uring the last few decades, the number of seniors over the age of 60 has increased significantly. A recent study from the United Nations has shown that the number of people aged 65 years or over will increase from 727 million in 2020 to 1.5 billion by 2050 [1]. Consequently, the propor- tion of the global population aged 65 years or over will increase from 9.3% in 2020 to 16% in 2050. In parallel with the aging of the world population, there has been an increase in age-relat- ed health issues, such as stroke, senso- rimotor disorders, Parkinson’s disease, and essential tremor, which signifi- cantly impact health-care systems. With a system that is underresourced, patients are transferred from hospitals to home while still suffering from ma- jor functional deficits. In this aging crisis, a potential solution is to develop technologies and techniques that can provide 1) efficient, effective, widely accessible, and affordable means of neurorehabilitation; and 2) intuitive and agile assistance to maximize the patients’ independence during activi- ties of daily living. Biosignal process- ing (BSP) plays an imperative role in the development of these advanced, intelligent, and dynamic rehabilitation and assistive solutions. Human–machine interfacing Neurorehabilitation and assistive tech- nologies are based on processing, decom- posing, and decoding of bioelectrical, biomechanical, and biochemical signals. The nonstationary and nonlinear nature of biological signals requires innovative techniques beyond conventional ap- proaches. The ultimate goal is to imple- ment practical and effective augmentation techniques for the sensorimotor capabilities of patients to achieve either 1) the instan- taneous replacement of lost functions (that is, assistive solution) or 2) the gradual en- hancement of the re- sidual functions (that is, rehabilitative so- lution). Achieving these goals require the development of human–machine in- terface (HMI) systems, which aim at the fusion of human neuromechanics and robotics. HMIs have been substantially im- proved by recent advances in BSP and machine learning (ML), in particular through the use of deep neural net- works. An ideal HMI should provide consistent, direct, intuitive, and accurate decoding of motor intent with minimal training and calibration. Furthermore, it should include sensory feedback mechanisms to enable bidirectional in- teraction, thus creating a closed control loop. Recent HMI systems, developed in research laboratories, have become increasingly sophisticated to address some of these goals. This has been achieved through the use of embedded mechatronic systems in combination with state-of-the-art ML modules and real-time BSP pipelines designed to achieve high robustness and versatil- ity. The design of increasingly complex HMI systems is a challenging multidis- ciplinary BSP problem. This special issue of IEEE Signal Process- ing Magazine (SPM) bridges several disci- plines, including signal processing, robotics, machine intelligence, neuroscience, and sta- tistics and control, applied to the domain of neurorehabil- itation, neuroprosthetics, and assistive systems. It describes advanced real- time processing of multichannel and multimodal biological signals—for ex- ample, electroencephalogram (EEG), video, speech, electronystagmography (ENG), electrocorticography (ECoG), and electromyography (EMG)—for effective and alternative treatments and diagnosis, with applications in neurore- habilitation, neuroprosthetics, wearable health technologies, and hybrid brain– computer interfaces. The articles in this special issue address these challenges and provide an overview of HMIs for up- per-limb prosthesis control, noninvasive Digital Object Identifier 10.1109/MSP.2021.3076280 Date of current version: 28 June 2021 Biosignal processing plays an imperative role in the development of these advanced, intelligent, and dynamic rehabilitation and assistive solutions. Signal Processing for Neurorehabilitation and Assistive Technologies