A tridirectional method for corticomuscular coupling analysis in Parkinson´s disease

Corticomuscular coupling analysis based on multiple datasets such as electroencephalography (EEG) and electromyography (EMG) signals provides a useful tool for understanding the underlying mechanisms of human motor control systems. In this work, we propose a tridirectional statistical modeling and analysis method to identify the coupling relationships between three types of datasets. Different from conventional approaches where only two datasets are considered and the interest is to interpret one dataset by another in a unidirectional fashion, the goal in this paper is to model three data spaces simultaneously in a tridirectional fashion. To address the intersubject variability concern in real-world medical applications, we further propose a group analysis framework based on the proposed method and apply it to concurrent EEG, EMG and behavior signals collected from 8 normal subjects and 9 patients with Parkinson´s disease (PD) performing a dynamic motor task. The results demonstrate highly correlated temporal patterns among the three types of signals and meaningful spatial activation patterns. The proposed approach is a promising technique for performing multi-subject and multi-modal data analysis.