Multivariate Kalman filter regression of confounding physiological signals for real-time classification of fNIRS data.
Multivariate Kalman filter regression of confounding physiological signals for real-time classification of fNIRS data.
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DOI：
10.1117/1.nph.9.2.025003
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发表时间：
2022-04
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影响因子：
5.3
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中科院分区：
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功能性近红外光谱（fNIRS）是一种无创技术，用于测量与神经功能相关的人体皮质血流动力学变化。由于 fNIRS 具有小型化潜力和相对较低的成本，fNIRS 已被提议用于脑机接口 (BCI) 等应用。与诱发神经活动产生的信号相比，脑外生理产生的信号幅度相对较大，这使得实时 fNIRS 信号解释具有挑战性。结合生理相关辅助信号（例如短分离通道）的回归技术通常用于将脑血流动力学响应与信号中的混杂成分分开。然而，脑外信号的耦合通常是非瞬时的，因此有必要找到适当的延迟来优化干扰消除。 我们提出了一种具有时间嵌入典型相关分析的卡尔曼滤波器的实现，用于具有考虑多个延迟的多元干扰回归量的 fNIRS 信号的实时回归。我们在之前获得的手指敲击数据集上测试了我们提出的方法，目的是将神经反应分类为左或右。我们展示了 24 通道 fNIRS 数据（每通道每秒 400 个样本）的计算高效实时处理，与非回归信号相比，心脏信号功率选择性降低了两个数量级，对比度噪声比提高了六倍。该方法为 fNIRS 的 BCI 应用提供了一种更好地实时区分大脑和非大脑信号的方法。
Functional near-infrared spectroscopy (fNIRS) is a noninvasive technique for measuring hemodynamic changes in the human cortex related to neural function. Due to its potential for miniaturization and relatively low cost, fNIRS has been proposed for applications, such as brain–computer interfaces (BCIs). The relatively large magnitude of the signals produced by the extracerebral physiology compared with the ones produced by evoked neural activity makes real-time fNIRS signal interpretation challenging. Regression techniques incorporating physiologically relevant auxiliary signals such as short separation channels are typically used to separate the cerebral hemodynamic response from the confounding components in the signal. However, the coupling of the extra-cerebral signals is often noninstantaneous, and it is necessary to find the proper delay to optimize nuisance removal. We propose an implementation of the Kalman filter with time-embedded canonical correlation analysis for the real-time regression of fNIRS signals with multivariate nuisance regressors that take multiple delays into consideration. We tested our proposed method on a previously acquired finger tapping dataset with the purpose of classifying the neural responses as left or right. We demonstrate computationally efficient real-time processing of 24-channel fNIRS data (400 samples per second per channel) with a two order of selective magnitude decrease in cardiac signal power and up to sixfold increase in the contrast-to-noise ratio compared with the nonregressed signals. The method provides a way to obtain better distinction of brain from non-brain signals in real time for BCI application with fNIRS.