Reliability, scalability and clinical viability are of utmost importance in the design of wireless Brain Machine Interface systems (BMIs). This paper reports on the design and implementation of a neuroprocessor for conditioning raw extracellular neural signals recorded through microelectrode arrays chronically implanted in the brain of awake behaving rats. The neuroprocessor design exploits a sparse representation of the neural signals to combat the limited wireless telemetry bandwidth. We demonstrate a multimodal processing capability (monitoring, compression, and spike sorting) inherent in the neuroprocessor to support a wide range of scenarios in real experimental conditions. A wireless transmission link with rate-dependent compression strategy is shown to preserve information fidelity in the neural data. At 32 channels, the neuroprocessor has been fully implemented on a 5mm×5mm nano-FPGA, and the prototyping resulted in 5.19 mW power consumption, bringing its performance within the power-size constraints for clinical use. The optimal design for compression and sorting performance was evaluated for multiple sampling frequencies, wavelet basis choice and power consumption.

中文翻译：

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用于无线、皮质控制的脑机接口应用的完全可植入、可编程和多模式神经处理器。

可靠性、可扩展性和临床可行性在无线脑机接口系统 (BMI) 的设计中至关重要。本文报告了神经处理器的设计和实现，用于调节通过长期植入清醒行为大鼠大脑中的微电极阵列记录的原始细胞外神经信号。神经处理器设计利用神经信号的稀疏表示来对抗有限的无线遥测带宽。我们展示了神经处理器固有的多模式处理能力（监控、压缩和尖峰排序），以支持真实实验条件下的各种场景。显示具有依赖于速率的压缩策略的无线传输链路以保持神经数据中的信息保真度。在 32 个频道，该神经处理器已在 5mm×5mm 纳米 FPGA 上完全实现，原型设计的功耗为 5.19 mW，使其性能在临床使用的功率尺寸限制范围内。针对多个采样频率、小波基选择和功耗评估了压缩和排序性能的最佳设计。