Peterson Victoria, Vissani Matteo, Luo Shiyu, Rabbani Qinwan, Crone Nathan E, Bush Alan, Richardson R Mark
Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States.
Instituto de Matemática Aplicada del Litoral, IMAL, UNL, CONICET, Santa Fe, Argentina.
Imaging Neurosci (Camb). 2024 Oct 1;2. doi: 10.1162/imag_a_00301. eCollection 2024.
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.
能够在清醒患者身上进行直接脑记录的神经外科手术为探索人类言语的神经生理学提供了独特的机会。这些机会的稀缺性以及参与患者的利他主义迫使我们在信号分析中采用最高的严谨性。在公开言语期间记录的颅内脑电图（iEEG）信号可能包含一个跟踪参与者语音基频（F0）的语音伪迹，涉及在言语产生和感知过程中被调制的相同高频伽马频率。为了解决这个伪迹，我们开发了一种空间滤波方法来识别和去除记录信号中由声学引起的污染。我们发现传统的参考方案会损害信号质量，而我们的数据驱动方法在保留潜在神经活动的同时对记录进行了去噪。
