来自各种来源的电干扰是使用多电极阵列神经记录系统收集的实验细胞外电生理学记录的常见问题。这种干扰会降低原始电生理信号的信噪比 (SNR)，并妨碍使用尖峰排序等技术进行数据后处理的准确性。在后处理期间以数字方式消除电干扰的传统信号处理方法包括带通滤波以将信号限制在生物数据的相关光谱范围内，例如尖峰频带 (300 Hz - 7kHz)、目标陷波滤波以消除来自生物数据的线路干扰。标准交流信号和公共参考去除，以最大限度地减少所有电极共有的噪声。这些方法需要先验了解干扰信号源的频率，以解决每个实验装置独特的电磁干扰环境。我们讨论了一种通过频谱峰值检测和消除 (SPDR) 步骤自动消除窄带电干扰的自适应方法，该方法可在记录数据的后处理过程中应用，该方法基于信号中高、窄带信号局部化的直觉频谱对应于干扰，而不是神经元的活动。频谱峰值突出度 (SPP) 阈值用于检测频域中的这些峰值，然后通过陷波滤波将其去除。我们将该方法应用于模拟波形以及从大脑类器官收集的实验电生理学数据，以证明其在不显着扭曲神经信号的情况下消除不需要的干扰的有效性。我们讨论了需要正确选择 SPP 阈值以避免过度过滤，过度过滤可能导致电生理学数据失真。我们还将滤波电生理学中的放电率活动与荧光钙成像（次级细胞活动标记物）进行比较，以量化信号失真并提供基于 SNR 的 SPP 阈值优化的界限。本文演示的自适应滤波技术是一种强大的方法，可以自动检测和消除记录的神经信号中的带间干扰，从而有可能在外部干扰源难以消除的更自然的环境中实现数据收集。版权所有 © 2023。由 Elsevier 发布公司

Electrical interference from various sources is a common issue for experimental extracellular electrophysiology recordings collected using multi-electrode array neural recording systems. This interference deteriorates the signal-to-noise ratio (SNR) of the raw electrophysiology signals and hampers the accuracy of data post-processing using techniques such as spike-sorting. Traditional signal processing methods to digitally remove electrical interference during post-processing include bandpass filtering to limit the signal to the relevant spectral range of the biological data, e.g., the spikes band (300 Hz - 7kHz), targeted notch filtering to remove line interference from standard alternating current signal, and common reference removal to minimize noise common to all electrodes. These methods require a priori knowledge of the frequency of the interfering signal source to address the unique electromagnetic interference environment of each experimental setup. We discuss an adaptive method for automatically removing narrow-band electrical interference through a spectral peak detection and removal (SPDR) step that can be applied during post-processing of the recorded data, based on the intuition that tall, narrowband signals localized in the signal spectrum correspond to interference, rather than the activity of neurons. A spectral peak prominence (SPP) threshold is used to detect these peaks in the frequency domain, which will then be removed via notch filtering. We applied this method to simulated waveforms and also experimental electrophysiology data collected from cerebral organoids to demonstrate its effectiveness for removing unwanted interference without significantly distorting the neural signals. We discuss that proper selection of the SPP threshold is required to avoid over-filtering, which can result in distortion of the electrophysiology data. We also compare the firing-rate activity in the filtered electrophysiology with fluorescence calcium imaging, a secondary cellular activity marker, to quantify signal distortion and provide bounds on SNR-based optimization of the SPP threshold. The adaptive filtering technique demonstrated in this paper is a powerful method that can automatically detect and remove interband interference in recorded neural signals, potentially enabling data collection in more naturalistic settings where external sources of interference are difficult to eliminate.Copyright © 2023. Published by Elsevier Inc.