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 3.2x 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.

翻译：植入式脑机接口（iBCI）在临床和日常应用中的重要性日益凸显。然而，无线iBCI在功耗和数据吞吐量方面面临严重限制。为缓解这些瓶颈，我们提出了一种具有自适应ADC采样和尖峰检测功能的无线iBCI头戴装置。与传统应用层压缩方案不同，我们采用服务器驱动架构实现源级效率。具体而言，服务器通过学习和优化电极特异性采样率向量，动态配置ADC硬件参数。该策略直接在采集层（ADC和放大器）降低数据量，而非依赖计算密集型的数字化后处理。跨不同受试者和阵列的广泛实验表明，该方法在运动任务和视觉任务中保持或超越解码精度的同时，实现了高达40 mW的功耗降低和3.2倍的FPGA资源利用率下降。该设计为长期在体记录提供了一种高度实用的解决方案。