Smetanin Nikolai, Belinskaya Anastasia, Lebedev Mikhail, Ossadtchi Alexei
Center for Bioelectric Interfaces, Higher School of Economics, Moscow, 101000, Russia.
J Neural Eng. 2020 Aug 4;17(4):046022. doi: 10.1088/1741-2552/ab890f.
The rapidly developing paradigm of closed-loop neuroscience has extensively employed brain rhythms as the signal forming real-time neurofeedback, triggering brain stimulation, or governing stimulus selection. However, the efficacy of brain rhythm contingent paradigms suffers from significant delays related to the process of extraction of oscillatory parameters from broad-band neural signals with conventional methods. To this end, real-time algorithms are needed that would shorten the delay while maintaining an acceptable speed-accuracy trade-off.
Here we evaluated a family of techniques based on the application of the least-squares complex-valued filter (LSCF) design to real-time quantification of brain rhythms. These techniques allow for explicit optimization of the speed-accuracy trade-off when quantifying oscillatory patterns. We used EEG data collected from 10 human participants to systematically compare LSCF approach to the other commonly used algorithms. Each method being evaluated was optimized by scanning through the grid of its hyperparameters using independent data samples.
When applied to the task of estimating oscillatory envelope and phase, the LSCF techniques outperformed in speed and accuracy both conventional Fourier transform and rectification based methods as well as more advanced techniques such as those that exploit autoregressive extrapolation of narrow-band filtered signals. When operating at zero latency, the weighted LSCF approach yielded 75% accuracy when detecting alpha-activity episodes, as defined by the amplitude crossing of the 95th-percentile threshold.
The LSCF approaches are easily applicable to low-delay quantification of brain rhythms. As such, these methods are useful in a variety of neurofeedback, brain-computer-interface and other experimental paradigms that require rapid monitoring of brain rhythms.
快速发展的闭环神经科学范式广泛采用脑节律作为形成实时神经反馈、触发脑刺激或控制刺激选择的信号。然而，脑节律相关范式的功效受到与使用传统方法从宽带神经信号中提取振荡参数过程相关的显著延迟的影响。为此，需要实时算法来缩短延迟，同时保持可接受的速度-精度权衡。
在这里，我们评估了一系列基于最小二乘复值滤波器（LSCF）设计应用于脑节律实时量化的技术。这些技术在量化振荡模式时允许对速度-精度权衡进行显式优化。我们使用从10名人类参与者收集的脑电图数据，系统地将LSCF方法与其他常用算法进行比较。通过使用独立数据样本扫描其超参数网格来优化每个被评估的方法。
当应用于估计振荡包络和相位的任务时，LSCF技术在速度和精度方面均优于传统的傅里叶变换和基于整流的方法，以及更先进的技术，如利用窄带滤波信号的自回归外推的技术。在零延迟操作时，加权LSCF方法在检测由第95百分位数阈值的幅度交叉定义的α活动事件时，准确率达到75%。
LSCF方法易于应用于脑节律的低延迟量化。因此，这些方法在各种神经反馈、脑机接口和其他需要快速监测脑节律的实验范式中很有用。
