一种用于言语产生过程中发音、大脑和肌肉活动的实时MRI视频、脑电图和表面肌电图同步采集方法
An Approach to Simultaneous Acquisition of Real-Time MRI Video, EEG, and Surface EMG for Articulatory, Brain, and Muscle Activity During Speech Production
AI总结：
本文首次实现了言语产生过程中实时MRI、EEG和表面EMG的同步采集，并提出了针对三模态设置的伪影抑制流程，为言语神经科学和脑机接口提供新视角。
摘要：
言语产生是一个复杂的过程，涉及神经规划、运动控制、肌肉激活和发音运动学。虽然声学语音信号是言语产生行为最易获取的产物，但它不能直接揭示其因果神经生理学基础。我们首次实现了实时（动态）MRI、EEG和表面EMG的同步采集，捕捉了言语产生链的几个关键方面：大脑信号、肌肉激活和发音运动。这种多模态采集范式带来了巨大的技术挑战，包括MRI引起的电磁干扰和肌源性伪影。为了缓解这些问题，我们引入了一种针对这种三模态设置的伪影抑制流程。一旦完全开发，该框架有望为言语神经科学提供前所未有的视角，并带来脑机接口的进步。源代码和数据已公开。
英文摘要：
Speech production is a complex process spanning neural planning, motor control, muscle activation, and articulatory kinematics. While the acoustic speech signal is the most accessible product of the speech production act, it does not directly reveal its causal neurophysiological substrates. We present the first simultaneous acquisition of real-time (dynamic) MRI, EEG, and surface EMG, capturing several key aspects of the speech production chain: brain signals, muscle activations, and articulatory movements. This multimodal acquisition paradigm presents substantial technical challenges, including MRI-induced electromagnetic interference and myogenic artifacts. To mitigate these, we introduce an artifact suppression pipeline tailored to this tri-modal setting. Once fully developed, this framework is poised to offer an unprecedented window into speech neuroscience and insights leading to brain-computer interface advances. The source code and data are available.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
