Hardware comparison.

We propose a novel 1-D median estimator specifically designed for the online detection of threshold-crossing signals, such as spikes in extracellular neural recordings. Compared to state-of-the-art algorithms, our method reduces estimator variance by up to eight times for a given buffer length. Likewise, for a given estimator variance, it requires a buffer length that is up to eight times smaller. This results in three significant advantages: the footprint area decreases by more than eight times, leading to reduced power consumption and a faster response to non-stationary signals.我们提出了一种专为越阈信号在线检测设计的新型一维中值估计器（1-D median estimator），此类信号例如细胞外神经记录中的锋电位尖峰。相较于当前最优算法，在给定缓存长度的条件下，本方法可将估计器方差最高降低8倍；同理，当保持估计器方差固定时，其所需的缓存长度最多可缩小至原先的1/8。这一特性带来三项显著优势：硬件占用面积减少8倍以上，进而降低了功耗，并能对非平稳信号实现更快的响应。