下一代无线脑机接口（BCI）设备需要专用的神经信号处理器（NSP）从而在给定的功耗和传输带宽限制下提取关键的神经信息。在神经学研究和临床应用中，尖峰检测和聚类是重要的信号处理步骤。本工作基于对硬件计算友好的尖峰检测和特征提取算法的系统评估，提出非线性能量算子（NEO）、一阶和二阶导数（FSDE）与“扰动”K均值聚类等算法，共同实现最优的准确性性能。NSP ASIC采用通道交织架构使功耗和面积最小化，在65纳米CMOS工艺技术下NSP每通道功率消耗2微瓦，面积占用0.0057平方毫米。所提出系统实现了92%的无监督尖峰分类准确率和98.3%的数据速率降低，显示了应用在高通道数无线BCI的巨大潜力。
Next generation of wireless brain-computer-interface (BCI) devices require dedicated neural signal processors (NSPs) to extract key neurological information while operating within given power consumption and transmission bandwidth limits. Spike detection and clustering are important signal processing steps in neurological research and clinical applications. Computational-friendly spike detection and feature extraction algorithms are first systematically evaluated in this work. The nonlinear energy operator (NEO) and the first-and-second-derivative (FSDE) together with the ‘perturbed’ K-mean clustering achieve the highest accuracy performance. An NSP ASIC is implemented in a channel-interleaved architecture and the folding ratio of 16 leads to the minimum power-and-area product. As the result, the NSP consumes 2-μW power consumption and occupies 0.0057 mm2 for each channel in a 65-nm CMOS technology. The proposed system achieves the unsupervised spike classification accuracy of 92% and a data-rate reduction of 98.3%, showing the promise for realizing high-channel-count wireless BCIs.
