A supervised data-driven spatial filter denoising method for speech artifacts in intracranial electrophysiological recordings.
A supervised data-driven spatial filter denoising method for speech artifacts in intracranial electrophysiological recordings.
复制标题
一种用于颅内电生理记录中语音伪影的监督数据驱动的空间滤波器去噪方法。
DOI：
10.1101/2023.04.05.535577
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发表时间：
2023
期刊：
影响因子：
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通讯作者：
MarkRichardson,R
中科院分区：
文献类型：
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作者：
Peterson,Victoria;Vissani,Matteo;Luo,Shiyu;Rabbani,Qinwan;Crone,NathanE;Bush,Alan;MarkRichardson,R
神经外科手术可以直接记录清醒患者的大脑，为探索人类言语的神经生理学提供了独特的机会。这些机会的稀缺性和参与患者的利他主义迫使我们对信号分析采用最高的严格性。在公开演讲期间记录的颅内脑电图 (iEEG) 信号可能包含跟踪参与者语音基频 (F0) 的语音伪影，涉及在语音产生和感知过程中调制的相同高伽马频率。为了解决这个问题，我们开发了一种空间过滤方法来识别和消除记录信号中声学引起的污染。我们发现传统的参考方案会危及信号质量，而我们的数据驱动方法对记录进行去噪，同时保留潜在的神经活动。
Neurosurgical procedures that enable direct brain recordings in awake patients offer unique opportunities to explore the neurophysiology of human speech. The scarcity of these opportunities and the altruism of participating patients compel us to apply the highest rigor to signal analysis. Intracranial electroencephalography (iEEG) signals recorded during overt speech can contain a speech artifact that tracks the fundamental frequency (F0) of the participant’s voice, involving the same high-gamma frequencies that are modulated during speech production and perception. To address this artifact, we developed a spatial-filtering approach to identify and remove acoustic-induced contaminations of the recorded signal. We found that traditional reference schemes jeopardized signal quality, whereas our data-driven method denoised the recordings while preserving underlying neural activity.