Integrating rhythmic auditory stimulation in intelligent rehabilitation technologies for enhanced post-stroke recovery
Stroke remains the leading cause of adult disability worldwide, resulting in long-term motor and cognitive impairments and imposing substantial socioeconomic burdens. Despite the widespread use of rehabilitation therapies, clinical outcomes remain suboptimal, underscoring the urgent need for more effective interventions to enhance neuroplasticity. This review explores the potential of integrating rhythmic auditory stimulation (RAS)-a music-based neurorehabilitation technique that leverages auditory-motor synchronization-with intelligent rehabilitation technologies such as robotics and virtual reality (VR). While various music-based interventions have shown promise in neurological recovery, this review focuses specifically on RAS due to its precise temporal structure, well-established neurophysiological mechanisms, and strong compatibility with technology-assisted platforms. We systematically examine the clinical evidence supporting RAS, evaluate the strengths and limitations of current intelligent rehabilitation systems, and discuss future directions for creating closed-loop, adaptive therapy paradigms. By combining RAS with robotic and VR-based interventions, we propose a novel framework for enhancing motor and cognitive recovery after stroke. This integrated approach offers new opportunities for personalized, engaging, and scalable neurorehabilitation strategies grounded in neuroscience, engineering, and clinical practice.
Authors
- Jianming Fu (ORCID: https://orcid.org/0000-0002-4639-5824)
- Yanyan Zhao (ORCID: https://orcid.org/0009-0005-8467-2801)
- Xu Han (ORCID: https://orcid.org/0000-0002-3963-4348)
Institutions
- Tongji University (CN)
- Anhui University (CN)
- Hefei University (CN)
- Jiaxing University (CN)
- First Hospital of Jiaxing (CN)
- Tongji Hospital (CN)
Publication Details
- Journal
- Frontiers in Bioengineering and Biotechnology
- Published
- 2025-08-29
- DOI
- https://doi.org/10.3389/fbioe.2025.1649011
- Citations
- 2
- Primary Topic
- EEG and Brain-Computer Interfaces
- Type
- article
- Field-Weighted Citation Impact
- 2.86
Funders
- Department of Education of Zhejiang Province