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Neurotechnology in Stroke Neurorehabilitation: Towards Precision and Personalized Rehabilitation Treatment

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Neurotechnology in Stroke Neurorehabilitation: Towards Precision and Personalized Rehabilitation Treatment

Tianyu Jia 1,2,† Jingyao Sun 2,† Yun Guo 3,† Chong Li 4,* Yu Pan 3,*

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1
Department of Bioengineering, Imperial College London, London W12 0BZ, UK
2
Department of Mechanical Engineering, Tsinghua University, Beijing 100084, China
3
Department of Rehabilitation Medicine, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua Medicine, Tsinghua University, Beijing 102218, China
4
School of Biomedical Engineering, Tsinghua University, Beijing 100084, China
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.

Received: 05 May 2026 Revised: 10 June 2026 Accepted: 09 July 2026 Published: 20 July 2026

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© 2026 The authors. This is an open access article under the Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/).

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Intell. Rehabil. Eng. 2026, 1(1), 10006; DOI: 10.70322/ire.2026.10006
ABSTRACT: Given the large global population of stroke survivors and limited rehabilitation resources, efficient treatments are urgently needed to help patients regain independence and reintegrate into society. In this review, we discuss how artificial intelligence and neurotechnology can be used to accelerate neurorehabilitation after stroke. First, we introduce neurorehabilitation mechanisms that provide the basis for neurotechnology development. Next, we describe how neurophysiological and neuroimaging biomarkers can be used for multimodal assessment and prognostic prediction. We then provide examples of brain-computer interface (BCI)-driven rehabilitation robots and BCI-triggered transcranial and peripheral neuromodulation for closed-loop rehabilitation training.
Keywords: Stroke rehabilitation; Neuromodulation; Rehabilitation robotics; Prognostic prediction; Brain-computer interface
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