微技术的进步使得可以同时记录的神经元数量呈指数趋势。然而,数据带宽需求随着信道计数而增加。涉及电生理学的绝大多数实验工作存储原始数据,然后离线处理;以检测潜在的尖峰事件。然而,新兴的应用程序需要新的方法,实时处理。
我们开发了一种自适应,低复杂度的尖峰检测算法,其结合了三个新颖的分量,用于:(1)去除局部场电势;(2)增强信噪比;以及(3)计算自适应阈值。所提出的算法已经针对硬件实现进行了优化(即最小化计算,转换为定点实现),并在低功耗嵌入式目标上进行了演示。
该算法已在合成数据集和真实记录上进行了验证,检测灵敏度高达90%。使用现成嵌入式平台的初始硬件实现表明,内存需求小于0.1kbROM和3kb程序闪存,消耗130μW的平均功率。
提出的方法比其他方法有优势,它允许以完全自主的方式从神经活动中实时可靠地检测尖峰事件,不需要任何校准,并且可以在低硬件资源下实现。
所提出的方法可以有效和自适应地检测尖峰。它减轻了重新校准的需要,这对于实现可行的BMI至关重要,未来的“高带宽”系统以1000个频道为目标,更是如此。
The progress in microtechnology has enabled an exponential trend in the number of neurons that can be simultaneously recorded. The data bandwidth requirement is however increasing with channel count. The vast majority of experimental work involving electrophysiology stores the raw data and then processes this offline; to detect the underlying spike events. Emerging applications however require new methods for local, real-time processing.
We have developed an adaptive, low complexity spike detection algorithm that combines three novel components for: (1) removing the local field potentials; (2) enhancing the signal-to-noise ratio; and (3) computing an adaptive threshold. The proposed algorithm has been optimised for hardware implementation (i.e. minimising computations, translating to a fixed-point implementation), and demonstrated on low-power embedded targets.
The algorithm has been validated on both synthetic datasets and real recordings yielding a detection sensitivity of up to 90%. The initial hardware implementation using an off-the-shelf embedded platform demonstrated a memory requirement of less than 0.1 kb ROM and 3 kb program flash, consuming an average power of 130 μW.
The method presented has the advantages over other approaches, that it allows spike events to be robustly detected in real-time from neural activity in a completely autonomous way, without the need for any calibration, and can be implemented with low hardware resources.
The proposed method can detect spikes effectively and adaptively. It alleviates the need for re-calibration, which is critical towards achieving a viable BMI, and more so with future \'high bandwidth\' systems\' targeting 1000s of channels.