Large-Scale Cortical Network Analysis and Classification of MI-BCI Tasks Based on Bayesian Nonnegative Matrix Factorization

• Published in: IEEE transactions on neural systems and rehabilitation engineering Vol. 32; pp. 2187 - 2197
• Main Authors: Yu, Shiqi, Mao, Bin, Zhou, Yuanhang, Liu, Yunhong, Yi, Chanlin, Li, Fali, Yao, Dezhong, Xu, Peng, San Liang, X., Zhang, Tao
• Format: Journal Article
• Language: English
• Published: United States IEEE 2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adult; Algorithms; Bayes methods; Bayes Theorem; Bayesian analysis; Bayesian NMF; Biomarkers; Brain modeling; Brain-Computer Interfaces; Cerebral Cortex - physiology; Classification; Clustering; Electroencephalography; Electroencephalography - methods; Factorization; Feature extraction; Female; Frequencies; Functional Laterality - physiology; functional network connectivity; Hand - physiology; Hemispheric laterality; Human-computer interface; Humans; Imagination - physiology; large-scale network; Location awareness; machine learning; Male; Mental task performance; Motor imagery; Motors; Movement - physiology; Nerve Net - physiology; Network analysis; Sensorimotor system; Task analysis; Young Adult
• ISSN: 1534-4320; 1558-0210; 1558-0210
• DOI: 10.1109/TNSRE.2024.3409872

Motor imagery (MI) is a high-level cognitive process that has been widely applied to clinical rehabilitation and brain-computer interfaces (BCIs). However, the decoding of MI tasks still faces challenges, and the neural mechanisms underlying its application are unclear, which seriously hinders the development of MI-based clinical applications and BCIs. Here, we combined EEG source reconstruction and Bayesian nonnegative matrix factorization (NMF) methods to construct large-scale cortical networks of left-hand and right-hand MI tasks. Compared to right-hand MI, the results showed that the significantly increased functional network connectivities (FNCs) mainly located among the visual network (VN), sensorimotor network (SMN), right temporal network, right central executive network, and right parietal network in the left-hand MI at the <inline-formula> <tex-math notation="LaTeX">\beta </tex-math></inline-formula> (13-30Hz) and all (8-30Hz) frequency bands. For the network properties analysis, we found that the clustering coefficient, global efficiency, and local efficiency were significantly increased and characteristic path length was significantly decreased in left-hand MI compared to right-hand MI at the <inline-formula> <tex-math notation="LaTeX">\beta </tex-math></inline-formula> and all frequency bands. These network pattern differences indicated that the left-hand MI may need more modulation of multiple large-scale networks (i.e., VN and SMN) mainly located in the right hemisphere. Finally, based on the spatial pattern network of FNC and network properties, we propose a classification model. The proposed model achieves a top classification accuracy of 78.2% in cross-subject two-class MI-BCI tasks. Overall, our findings provide new insights into the neural mechanisms of MI and a potential network biomarker to identify MI-BCI tasks.