Multi-Granularity Analysis of Brain Networks Assembled With Intra-Frequency and Cross-Frequency Phase Coupling for Human EEG After Stroke - Ren, Bin; Yang, Kun; Zhu, Li; Hu, Lang; Qiu, Tao; Kong, Wanzeng; Zhang, Jianhai (Frontiers in Computational Neuroscience, 2022)
Evaluating the impact of stroke on the human brain based on electroencephalogram (EEG) remains a challenging problem. Previous researches are mainly analyzed within frequency bands. This paper proposes a multi-granularity analysis framework, which uses multiple brain networks assembled with intra-frequency and cross-frequency phase-phase coupling to evaluate the stroke impact in temporal and spatial granularity. Through our experiments on the EEG data of 11 left ischemic stroke patients and 11 healthy controls during the mental rotation task, we found that the brain information interaction is highly affected after stroke, especially in delta-related cross-frequency bands, like delta-alpha, delta-low beta, and delta-high beta. Besides, the average PSI of the right hemisphere between stroke patients and controls has a significant difference, especially in delta-alpha (p=0.0186 in the right-hand mental rotation task, p=0.0166 in the left-hand mental rotation task), which shows that the non-lesion hemisphere of stroke patients is also affected while it cannot be observed in intra-frequency bands. The graph theory analysis of the entire task stage reveals that stroke patients' brain network has a longer feature path length and smaller clustering coefficient. Besides, in the graph theory analysis of three sub-stags, the more stable significant difference between the two groups is emerging in the mental rotation sub-stage (500ms-800ms). These findings demonstrate that the coupling between different frequency bands brings a new perspective to understanding the brain's cognitive process after stroke.