Efficacy, Neuromechanisms, and Human-AI Interface Integration: Neural-Adaptation Training Paradigms for Sports and Rehabilitation

Neural-adaptation training paradigms (NATP)—including mirror therapy (MT), motor imagery training (MIT), and intermanual transfer (IT)—are increasingly applied in both athletic and rehabilitation contexts to improve motor skill acquisition and reacquisition. These paradigms leverage neuroplasticity to facilitate functional improvement without direct physical movement. While numerous studies report moderate to high efficacy, some evidence highlights inconsistencies when these strategies are applied individually or combined. This discrepancy underscores the need for a systematic investigation into their efficacy, underlying neuromechanisms, and optimization.

Recent advances in Artificial Intelligence (AI) and Brain-Computer Interfaces (BCI) offer new frontiers for NATP. Integration of real-time neural feedback, AI-driven adaptive protocols, and brain-computer interfaces may allow for personalized, dynamic training environments. These technologies can enhance patient engagement, enable non-verbal neurofeedback, and facilitate deeper understanding of the neural processes driving adaptation.

This research topic invites interdisciplinary contributions exploring not only the classical neural-adaptation paradigms but also their augmentation through AI-driven tools, sensorimotor feedback systems, and brain-machine interfaces. The goal is to refine NATP using intelligent, interactive technologies that can adapt to individual neurological profiles and functional goals in both clinical and sports performance domains.

To deepen our understanding of the efficacy and neuromechanisms underlying Neural-Adaptation Training Paradigms (NATP), we invite contributions addressing—but not limited to—the following themes:

• Empirical studies on NATP techniques, including mirror therapy, motor imagery training, and bilateral transfer.

• Investigations into the effects of NATP on motor skill acquisition and reacquisition across both healthy and clinical populations.

• Development and evaluation of AI-augmented NATP platforms, such as VR/AR-based mirror therapy systems incorporating neural feedback.

• Implementation of brain–computer interface (BCI) technologies that deliver real-time feedback during motor imagery training.

• AI-driven predictive models for motor recovery and skill acquisition trajectories, utilizing neurophysiological signals (e.g., EEG, fNIRS, fMRI).

• Integration of sensor-based technologies and machine learning algorithms to dynamically adjust training intensity, task complexity, or sensory feedback.

• Comparative studies examining the efficacy of conventional versus AI-enhanced NATP interventions in populations with stroke, Parkinson’s disease, orthopedic injuries, or in high-performance sports settings.

• Narrative or systematic reviews synthesizing current evidence across these research domains.