Objective High-frequency band (HFB) activity, measured using implanted sensors over the cortex, is increasingly considered as a feature for the study of brain function and the design of neural-implants, such as Brain-Computer Interfaces (BCIs). One common way of extracting these power signals is using a wavelet dictionary, which involves the selection of different temporal sampling and temporal smoothing parameters, such that the resulting HFB signal best represents the temporal features of the neuronal event of interest. Typically, the use of neuro-electrical signals for closed-loop BCI control requires a certain level of signal downsampling and smoothing in order to remove uncorrelated noise, optimize performance and provide fast feedback. However, a fixed setting of the sampling and smoothing parameters may lead to a suboptimal representation of the underlying neural responses and poor BCI control. This problem can be resolved with a systematic assessment of parameter settings. Approach With classification of HFB power responses as performance measure, different combinations of temporal sampling and temporal smoothing values were applied to data from sensory and motor tasks recorded with high-density and standard clinical electrocorticography (ECoG) grids in 12 epilepsy patients. Main results The results suggest that HFB ECoG responses are best performed with high sampling and subsequent smoothing. For the paradigms used in this study, optimal temporal sampling ranged from 29 Hz to 50 Hz. Regarding optimal smoothing, values were similar between tasks (0.1-0.9 s), except for executed complex hand gestures, for which two optimal possible smoothing windows were found (0.4-0.6 s and 0.9-2.7 s). Significance The range of optimal values indicates that parameter optimization depends on the functional paradigm and may be subject-specific. Our results advocate a methodical assessment of parameter settings for optimal decodability of ECoG signals.

中文翻译：

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采样率和平滑的优化改进了脑电信号中高频功率的分类

目标 使用植入皮层的传感器测量的高频带 (HFB) 活动越来越多地被认为是研究大脑功能和设计神经植入物（如脑机接口 (BCI)）的一个特征。提取这些功率信号的一种常见方法是使用小波字典，这涉及选择不同的时间采样和时间平滑参数，以便生成的 HFB 信号最能代表感兴趣的神经元事件的时间特征。通常，将神经电信号用于闭环 BCI 控制需要一定程度的信号下采样和平滑处理，以消除不相关的噪声、优化性能并提供快速反馈。然而，采样和平滑参数的固定设置可能导致潜在神经反应的次优表示和较差的 BCI 控制。这个问题可以通过参数设置的系统评估来解决。方法 将 HFB 功率响应分类为性能测量，将时间采样和时间平滑值的不同组合应用于来自 12 名癫痫患者的高密度和标准临床皮质电图 (ECoG) 网格记录的感觉和运动任务的数据。主要结果 结果表明，HFB ECoG 响应最好通过高采样和随后的平滑来执行。对于本研究中使用的范例，最佳时间采样范围为 29 Hz 至 50 Hz。关于最佳平滑，任务之间的值相似（0.1-0.9 s），除了执行的复杂手势之外，找到了两个可能的最佳平滑窗口（0.4-0.6 s 和 0.9-2.7 s）。意义 最佳值的范围表明参数优化取决于功能范式并且可能是特定于主题的。我们的结果提倡对参数设置进行系统评估，以实现 ECoG 信号的最佳可解码性。