Wang Xiulin, Liu Wenya, Wang Xiaoyu, Mu Zhen, Xu Jing, Chang Yi, Zhang Qing, Wu Jianlin, Cong Fengyu
Department of Radiology, Affiliated Zhongshan Hospital of Dalian University, Dalian, China.
School of Biomedical Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, China.
Front Hum Neurosci. 2021 Dec 15;15:799288. doi: 10.3389/fnhum.2021.799288. eCollection 2021.
Ongoing electroencephalography (EEG) signals are recorded as a mixture of stimulus-elicited EEG, spontaneous EEG and noises, which poses a huge challenge to current data analyzing techniques, especially when different groups of participants are expected to have common or highly correlated brain activities and some individual dynamics. In this study, we proposed a data-driven shared and unshared feature extraction framework based on nonnegative and coupled tensor factorization, which aims to conduct group-level analysis for the EEG signals from major depression disorder (MDD) patients and healthy controls (HC) when freely listening to music. Constrained tensor factorization not only preserves the multilinear structure of the data, but also considers the common and individual components between the data. The proposed framework, combined with music information retrieval, correlation analysis, and hierarchical clustering, facilitated the simultaneous extraction of shared and unshared spatio-temporal-spectral feature patterns between/in MDD and HC groups. Finally, we obtained two shared feature patterns between MDD and HC groups, and obtained totally three individual feature patterns from HC and MDD groups. The results showed that the MDD and HC groups triggered similar brain dynamics when listening to music, but at the same time, MDD patients also brought some changes in brain oscillatory network characteristics along with music perception. These changes may provide some basis for the clinical diagnosis and the treatment of MDD patients.
持续的脑电图（EEG）信号被记录为刺激诱发的脑电图、自发脑电图和噪声的混合信号，这给当前的数据分析技术带来了巨大挑战，尤其是当期望不同组的参与者具有共同或高度相关的大脑活动以及一些个体动态变化时。在本研究中，我们提出了一种基于非负和耦合张量分解的数据驱动的共享和非共享特征提取框架，旨在对重度抑郁症（MDD）患者和健康对照（HC）在自由听音乐时的脑电信号进行组水平分析。约束张量分解不仅保留了数据的多线性结构，还考虑了数据之间的共同和个体成分。所提出的框架与音乐信息检索、相关性分析和层次聚类相结合，有助于同时提取MDD组和HC组之间/内的共享和非共享时空谱特征模式。最后，我们获得了MDD组和HC组之间的两个共享特征模式，并从HC组和MDD组总共获得了三个个体特征模式。结果表明，MDD组和HC组在听音乐时触发了相似的脑动力学，但同时，MDD患者在音乐感知过程中也带来了一些脑振荡网络特征的变化。这些变化可能为MDD患者的临床诊断和治疗提供一些依据。
