⚡ 本页包含 AI 生成的分析内容，仅供参考
该论文提出一种用于闭环神经调节的斩波稳定生物信号放大器，能够在存在大刺激伪影的情况下记录微小神经信号。通过提升输入阻抗至300MΩ并实现40mVpp线性输入范围，同时仅消耗2µW功耗，解决了传统前端难以同时处理大信号和保持信号完整性的问题。
Modern neuromodulation requires closed-loop functionality, where neural recordings are used to adapt stimulation patterns in real time. A closed-loop system requires the neural sensing front-end to record small neural signals in the presence of large stimulation artifacts. The amplitude of artifacts can be a few 10s of mV, and their power is usually in the same frequency band as the signals of interest, requiring non-traditional adaptive filtering to attenuate the artifacts. This requires a sensing front-end that can handle large signals while maintaining the signal integrity of the accompanying small neural signals. State-of-the-art front-ends saturate beyond an input of ~5mV and have limited linearity, making them incapable of handling large artifacts. This work presents a front-end that can tolerate up to ±20mV artifacts in the signal band of 1Hz to 5kHz. To digitize a 1mV neural signal to 8 bits in the presence of a 20mV artifact,
Hariprasad Chandrakumar, Dejan Marković
University of California, Los Angeles, CA