Implantable Brain-Computer Interfaces (iBCIs) are increasingly pivotal in clinical and daily applications. However, wireless iBCIs face severe constraints in power consumption and data throughput. To mitigate these bottlenecks, we propose a wireless iBCI headstage featuring adaptive ADC sampling and spike detection. Distinguishing our design from traditional application-layer compression, we employ a server-driven architecture that achieves source-level efficiency. Specifically, the server learns an optimal, electrode-specific sample rate vector to dynamically reconfigure the ADC hardware. This strategy reduces data volume directly at the acquisition layer (ADC and amplifier) rather than relying on computationally intensive post-digitization processing. Extensive experiments across diverse subjects and arrays demonstrate a power reduction of up to 40 mW and a 3.2$\times$ decrease in FPGA resource utilization, all while maintaining or exceeding decoding accuracy in both motor and visual tasks. This design offers a highly practical solution for long-term in-vivo recording.Our prototype is open-sourced in: https://github.com/liuhongyao99cs/32-Channel-Wireless-BCI-Headstage.

翻译：植入式脑机接口（iBCIs）在临床和日常应用中日渐关键。然而，无线iBCIs面临功耗和数据吞吐量的严苛约束。为缓解这些瓶颈，我们提出一种采用自适应ADC采样和尖峰检测的无线iBCI头戴装置。与传统应用层压缩不同，本设计采用服务器驱动架构以达成源级效率。具体而言，服务器学习一个最优的、针对特定电极的采样率向量，用于动态重构ADC硬件。该策略直接在采集层（ADC和放大器）减少数据量，而非依赖数字化后的计算密集型处理。跨被试和电极阵列的大量实验表明，在运动与视觉任务中，本设计在维持或超越解码精度的同时，功耗降低高达40 mW，FPGA资源占用率降低3.2倍。该设计为长期在体记录提供了一种高实用性的解决方案。我们的原型已在以下地址开源：https://github.com/liuhongyao99cs/32-Channel-Wireless-BCI-Headstage