Monitoring speech tasks with functional near-infrared spectroscopy (fNIRS) enables investigation of speech production mechanisms and informs treatment strategies for speech-related disorders such as stuttering. Unfortunately, due to movement of the temporalis muscle, speech production can induce relative movement between probe optodes and skin. These movements generate motion artifacts during speech tasks. In practice, spurious hemodynamic responses in functional activation signals arise from lack of information about the consequences of speech-related motion artifacts, as well as from lack of standardized processing procedures for fNIRS signals during speech tasks. To this end, we characterize the effects of speech production on fNIRS signals, and we introduce a systematic analysis to ameliorate motion artifacts. The study measured 50 healthy subjects performing jaw movement (JM) tasks and found that JM produces two different patterns of motion artifacts in fNIRS. To remove these unwanted contributions, we validate a hybrid motion-correction algorithm based sequentially on spline interpolation and then wavelet filtering. We compared performance of the hybrid algorithm with standard algorithms based on spline interpolation only and wavelet decomposition only. The hybrid algorithm corrected 94% of the artifacts produced by JM, and it did not lead to spurious responses in the data. We also validated the hybrid algorithm during a reading task performed under two different conditions: reading aloud and reading silently. For both conditions, we observed significant cortical activation in brain regions related to reading. Moreover, when comparing the two conditions, good agreement of spatial and temporal activation patterns was found only when data were analyzed using the hybrid approach. Overall, the study demonstrates a standardized processing scheme for fNIRS data during speech protocols. The scheme decreases spurious responses and intersubject variability due to motion artifacts.

中文翻译：

---

语音协议的功能性近红外光谱：运动伪影的表征和改善数据分析的指南。

使用功能性近红外光谱（fNIRS）监视语音任务可以研究语音产生机制，并为诸如口吃等与语音相关的疾病提供治疗策略。不幸的是，由于颞肌的运动，语音产生会引起探针视极和皮肤之间的相对运动。这些运动在语音任务期间产生运动伪像。在实践中，功能激活信号中的虚假血液动力学响应是由于缺乏有关语音相关运动伪像后果的信息，以及缺乏语音任务期间针对fNIRS信号的标准化处理程序而引起的。为此，我们表征了语音产生对fNIRS信号的影响，并引入了系统分析来改善运动伪像。这项研究对50名健康的受试者进行了下颌运动（JM）任务，并发现他们在fNIRS中产生了两种不同的运动伪影模式。为了消除这些不必要的影响，我们依次验证了基于样条插值和小波滤波的混合运动校正算法。我们将混合算法的性能与仅基于样条插值和仅基于小波分解的标准算法进行了比较。混合算法纠正了JM产生的伪像的94％，并且没有导致数据中的虚假响应。我们还验证了在两种不同条件下执行的阅读任务期间的混合算法：大声阅读和静默阅读。对于这两种情况，我们都观察到与阅读有关的大脑区域有明显的皮质激活。此外，比较这两个条件时，只有在使用混合方法分析数据时，才能发现空间和时间激活模式具有良好的一致性。总体而言，该研究表明了语音协议期间fNIRS数据的标准化处理方案。该方案减少了由于运动伪像引起的虚假响应和主体间可变性。