Understanding the impact of stroke on the human brain is a tough nut to crack, especially when it comes to evaluating it through electroencephalogram (EEG) data. But fear not! Researchers have come up with a new approach called multi-granularity analysis. Imagine you have a plate of pancakes, each representing a different frequency band in the brain. Instead of just analyzing each pancake individually, this framework combines them to create a delicious stack of brain networks. By examining the interactions between different frequency bands, they discovered that stroke heavily affects the way information is communicated in the brain. Specifically, they found that delta-related cross-frequency bands, like delta-alpha and delta-beta, were significantly impacted. They also noticed that even the non-lesion hemisphere of stroke patients showed changes in delta-alpha synchronization compared to healthy controls. Additionally, graph theory analysis revealed differences in brain network characteristics between the two groups, with stroke patients having longer path lengths and smaller clustering coefficients. The researchers suggest that this new perspective on brain dynamics after stroke could enhance our understanding of post-stroke cognitive processes. So dig in and explore the full article for all the fascinating details!

Evaluating the impact of stroke on the human brain based on electroencephalogram (EEG) remains a challenging problem. Previous studies are mainly analyzed within frequency bands. This article 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 patients with left ischemic stroke and 11 healthy controls during the mental rotation task, we find that the brain information interaction is highly affected after stroke, especially in delta-related cross-frequency bands, such as delta-alpha, delta-low beta, and delta-high beta. Besides, the average phase synchronization index (PSI) of the right hemisphere between patients with stroke and controls has a significant difference, especially in delta-alpha (p = 0.0186 in the left-hand mental rotation task, p = 0.0166 in the right-hand mental rotation task), which shows that the non-lesion hemisphere of patients with stroke is also affected while it cannot be observed in intra-frequency bands. The graph theory analysis of the entire task stage reveals that the brain network of patients with stroke 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 (500–800 ms). These findings demonstrate that the coupling between different frequency bands brings a new perspective to understanding the brain’s cognitive process after stroke.

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