Zhou Di, Zhang Gaoyan, Dang Jianwu, Unoki Masashi, Liu Xin
School of Information Science, Japan Advanced Institute of Science and Technology, Ishikawa, Japan.
College of Intelligence and Computing, Tianjin Key Laboratory of Cognitive Computing and Application, Tianjin University, Tianjin, China.
Front Comput Neurosci. 2022 Jul 7;16:919215. doi: 10.3389/fncom.2022.919215. eCollection 2022.
In recent years, electroencephalograph (EEG) studies on speech comprehension have been extended from a controlled paradigm to a natural paradigm. Under the hypothesis that the brain can be approximated as a linear time-invariant system, the neural response to natural speech has been investigated extensively using temporal response functions (TRFs). However, most studies have modeled TRFs in the electrode space, which is a mixture of brain sources and thus cannot fully reveal the functional mechanism underlying speech comprehension. In this paper, we propose methods for investigating the brain networks of natural speech comprehension using TRFs on the basis of EEG source reconstruction. We first propose a functional hyper-alignment method with an additive average method to reduce EEG noise. Then, we reconstruct neural sources within the brain based on the EEG signals to estimate TRFs from speech stimuli to source areas, and then investigate the brain networks in the neural source space on the basis of the community detection method. To evaluate TRF-based brain networks, EEG data were recorded in story listening tasks with normal speech and time-reversed speech. To obtain reliable structures of brain networks, we detected TRF-based communities from multiple scales. As a result, the proposed functional hyper-alignment method could effectively reduce the noise caused by individual settings in an EEG experiment and thus improve the accuracy of source reconstruction. The detected brain networks for normal speech comprehension were clearly distinctive from those for non-semantically driven (time-reversed speech) audio processing. Our result indicates that the proposed source TRFs can reflect the cognitive processing of spoken language and that the multi-scale community detection method is powerful for investigating brain networks.
近年来，关于言语理解的脑电图（EEG）研究已从受控范式扩展到自然范式。在大脑可近似为线性时不变系统的假设下，利用时间响应函数（TRF）对自然言语的神经反应进行了广泛研究。然而，大多数研究在电极空间中对TRF进行建模，电极空间是脑源的混合体，因此无法完全揭示言语理解背后的功能机制。在本文中，我们提出了基于EEG源重建使用TRF来研究自然言语理解脑网络的方法。我们首先提出一种带有加法平均法的功能超对齐方法来降低EEG噪声。然后，我们基于EEG信号重建脑内的神经源，以估计从言语刺激到源区域的TRF，接着基于社区检测方法在神经源空间中研究脑网络。为了评估基于TRF的脑网络，在正常言语和时间反转言语的故事聆听任务中记录EEG数据。为了获得可靠的脑网络结构，我们从多个尺度检测基于TRF的社区。结果表明，所提出的功能超对齐方法能够有效降低EEG实验中个体设置引起的噪声，从而提高源重建的准确性。检测到的正常言语理解脑网络与非语义驱动（时间反转言语）音频处理的脑网络明显不同。我们的结果表明，所提出的源TRF能够反映口语的认知处理过程，并且多尺度社区检测方法在研究脑网络方面很强大。
