An Efficient Wireless iBCI Headstage with Adaptive ADC Sample Rate

TL;DR

This paper introduces a wireless iBCI headstage with adaptive sampling that reduces power and resource use by dynamically adjusting ADC rates based on server feedback, enhancing long-term recording viability.

Contribution

The novel adaptive ADC sampling approach driven by server learning optimizes data acquisition efficiency in wireless iBCIs, surpassing traditional compression methods.

Findings

Power consumption reduced by up to 40 mW.

FPGA resource utilization decreased by 3.2 times.

Decoding accuracy maintained or improved across tasks.

Abstract

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.