Dastin-van Rijn Evan M, Provenza Nicole R, Vogt Gregory S, Avendano-Ortega Michelle, Sheth Sameer A, Goodman Wayne K, Harrison Matthew T, Borton David A
Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN, United States.
Department of Neurosurgery, Baylor College of Medicine, Houston, TX, United States.
Front Hum Neurosci. 2022 Jul 7;16:934063. doi: 10.3389/fnhum.2022.934063. eCollection 2022.
Recent advances in wireless data transmission technology have the potential to revolutionize clinical neuroscience. Today sensing-capable electrical stimulators, known as "bidirectional devices", are used to acquire chronic brain activity from humans in natural environments. However, with wireless transmission come potential failures in data transmission, and not all available devices correctly account for missing data or provide precise timing for when data losses occur. Our inability to precisely reconstruct time-domain neural signals makes it difficult to apply subsequent neural signal processing techniques and analyses. Here, our goal was to accurately reconstruct time-domain neural signals impacted by data loss during wireless transmission. Towards this end, we developed a method termed Periodic Estimation of Lost Packets (PELP). PELP leverages the highly periodic nature of stimulation artifacts to precisely determine when data losses occur. Using simulated stimulation waveforms added to human EEG data, we show that PELP is robust to a range of stimulation waveforms and noise characteristics. Then, we applied PELP to local field potential (LFP) recordings collected using an implantable, bidirectional DBS platform operating at various telemetry bandwidths. By effectively accounting for the timing of missing data, PELP enables the analysis of neural time series data collected wireless transmission-a prerequisite for better understanding the brain-behavior relationships underlying neurological and psychiatric disorders.
无线数据传输技术的最新进展有可能彻底改变临床神经科学。如今，具有传感能力的电刺激器，即所谓的“双向设备”，被用于在自然环境中获取人类的慢性脑活动。然而，随着无线传输的出现，数据传输可能会出现故障，而且并非所有可用设备都能正确处理丢失的数据或提供数据丢失发生时的精确时间。我们无法精确重建时域神经信号，这使得后续神经信号处理技术和分析的应用变得困难。在此，我们的目标是准确重建在无线传输过程中受数据丢失影响的时域神经信号。为此，我们开发了一种名为“丢失数据包的周期性估计（PELP）”的方法。PELP利用刺激伪迹的高度周期性来精确确定数据丢失发生的时间。通过将模拟刺激波形添加到人类脑电图数据中，我们表明PELP对一系列刺激波形和噪声特征具有鲁棒性。然后，我们将PELP应用于使用在各种遥测带宽下运行的植入式双向深部脑刺激（DBS）平台收集的局部场电位（LFP）记录。通过有效考虑丢失数据的时间，PELP能够分析通过无线传输收集的神经时间序列数据——这是更好地理解神经和精神疾病背后的脑-行为关系的先决条件。
