AI-integrated closed-loop brain-machine interfaces using soft bioelectronics

Social and clinical demands for restoring lost neurological functions are rising due to the increasing prevalence of neurodegenerative diseases, paralysis, and aging populations. In response, brain-machine interfaces (BMIs) have emerged as a promising technology, with recent advances demonstrating substantial progress toward clinical translation. These advances are exemplified by high-channel neural interfaces and fully implantable systems developed by both academic and industrial efforts, including recent industry-led fully implantable BMIs. Despite these advances, current BMI systems still face critical limitations across multiple aspects. Mechanical mismatch between rigid electrodes and soft neural tissue at the material level induces chronic inflammation and progressive signal degradation, ultimately compromising long-term performance. This limitation extends to the device level, where insufficient structural adaptability hinders stable interfacing under dynamic physiological conditions. Building upon these challenges, conventional open-loop systems at the system level fail to account for temporal and inter-individual variability, thereby restricting the realization of truly personalized neuromodulation. Here, we review recent progress in addressing these challenges through the integration of soft and adaptive materials, which enable mechanically compliant, minimally invasive, and structurally adaptive neural interfaces capable of maintaining stable interactions with brain tissue over extended periods. Importantly, these material and device-level innovations are increasingly related to system-level advancements, including closed-loop systems enabling real-time feedback and AI-assisted decoding techniques. Advanced AI technique requires large volumes of high-quality neural data collected from high density of electrode arrays to achieve reliable decoding and personalized neuromodulation. In this context, tissue modulus matching and chronically stable soft interfaces play a critical role in reducing signal degradation and long-term data reliability. Such integration between material, device and system allows continuous monitoring and dynamic modulation of neural activity. This enables more precise, personalized, and context-aware brain-machine interactions, leading to advanced AI-based soft closed-loop bioelectronics for BMIs.