Upper extremity motor training of a subject with initially motor complete chronic high tetraplegia using constraint-induced biofeedback therapy

Advancing clinical understanding of surface electromyography biofeedback: bridging research, teaching, and commercial applications

INTRODUCTION

Expanding the use of surface electromyography-biofeedback (EMG-BF) devices in different therapeutic settings highlights the gradually evolving role of visualizing muscle activity in the rehabilitation process. This review evaluates their concepts, uses, and trends, combining evidence-based research.

AREAS COVERED

This review dissects the anatomy of EMG-BF systems, emphasizing their transformative integration with machine-learning (ML) and deep-learning (DL) paradigms. Advances such as the application of sophisticated DL architectures for high-density EMG data interpretation, optimization techniques for heightened DL model performance, and the fusion of EMG with electroencephalogram (EEG) signals have been spotlighted for enhancing biomechanical analyses in rehabilitation. The literature survey also categorizes EMG-BF devices based on functionality and clinical usage, supported by insights from commercial sectors.

EXPERT OPINION

The current landscape of EMG-BF is rapidly evolving, chiefly propelled by innovations in artificial intelligence (AI). The incorporation of ML and DL into EMG-BF systems augments their accuracy, reliability, and scope, marking a leap in patient care. Despite challenges in model interpretability and signal noise, ongoing research promises to address these complexities, refining biofeedback modalities. The integration of AI not only predicts patient-specific recovery timelines but also tailors therapeutic interventions, heralding a new era of personalized medicine in rehabilitation and emotional detection.

Functional neurological disorder: Extending the diagnosis to other disorders, and proposing an alternate disease term—Attentionally-modifiable disorder

BACKGROUND: The term “functional neurological disorder,” or “FND,” applies to disorders whose occurrence of neurological symptoms fluctuate with the patient’s attention to them. However, many other disorders that are not called “FND” nonetheless can also follow this pattern. Consequently, guidelines are unclear for diagnosing “FND.” OBJECTIVE: To review the neurological conditions that follow this pattern, but which have not so far been termed “FND,” to understand their overlap with conditions that have been termed “FND,” and to discuss the rationale for why FND has not been diagnosed for them. METHOD: A systematic review of the PubMed literature registry using the terms “fluctuation,” “inconsistency,” or “attention” did not yield much in the way of these candidate disorders. Consequently, this review instead relied on the author’s personal library of peer-reviewed studies of disorders that have resembled FND but which were not termed this way, due to his longstanding interest in this problem. Consequently, this approach was not systematic and was subjective regarding disease inclusion. RESULTS: This review identified numerous, diverse conditions that generally involve fluctuating neurological symptoms that can vary with the person’s attention to them, but which have not been called “FND.” The literature was unclear for reasons for not referring to “FND” in these instances. CONCLUSION: Most likely because of historical biases, the use of the term “FND” has been unnecessarily restricted. Because at its core FND is an attentionally-influenced disorder that can respond well to behavioral treatments, the field of neurological rehabilitation could benefit by extending the range of conditions that could be considered as “FND” and referred for similar behavioral treatments. Because the term “FND” has been viewed unfavorably by some patients and clinical practitioners and whose treatment is not implied, the alternative term attentionally-modifiable disorder is proposed.