Adaptive spike detection and hardware optimization towards autonomous, high-channel-count BMIs
Adaptive spike detection and hardware optimization towards autonomous, high-channel-count BMIs
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DOI：
10.1016/j.jneumeth.2021.109103
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发表时间：
2021-02
影响因子：
3
通讯作者：
Zheng Zhang;T. Constandinou
中科院分区：
文献类型：
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作者：
Zheng Zhang;T. Constandinou
背景微技术的进步使得可同时记录的神经元数量呈指数趋势。然而，数据带宽要求随着通道数量的增加而增加。绝大多数涉及电生理学的实验工作都会存储原始数据，然后离线处理；检测潜在的尖峰事件。然而，新兴应用需要新的本地实时处理方法。新方法我们开发了一种自适应、低复杂度尖峰检测算法，该算法结合了三个新颖的组件，用于：（1）消除局部场电位； (2)增强信噪比； (3)计算自适应阈值。所提出的算法已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。主要结果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 0.1kb ROM 和 3kb 程序闪存，平均功耗为 130 μW。与现有方法的比较所提出的方法比其他方法具有优势，它允许以完全自主的方式从神经活动中实时鲁棒地检测尖峰事件，无需任何校准，并且可以用较低的硬件资源实现。结论所提出的方法可以有效地检测尖峰。并自适应。它减少了重新校准的需要，这对于实现可行的 BMI 至关重要，对于未来针对数千个通道的“高带宽”系统来说更是如此。
BackgroundThe 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.New MethodsWe 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.Main resultsThe 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.Comparison with Existing MethodsThe 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.ConclusionThe 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.