Evaluating brain electroencephalogram signal dynamics across cognitive disorders using information geometry

Dementia, including Alzheimer’s disease and frontotemporal dementia, is a progressive brain disorder that disrupts memory, thinking, and behavior, with early diagnosis being critical for effective intervention. This study examines the alteration of brain activity caused by dementia by analyzing electroencephalogram (EEG) signals using an information geometry method known as information rate, which captures the evolving patterns of brain signals over time rather than relying on static averages. This method is applied across standard EEG frequency bands – delta, theta, alpha, beta, and gamma – in participants with dementia and healthy controls. The characteristics of the distribution of information rate are studied through the statistical moments (such as mean, variance, skewness, and kurtosis) and Shannon entropy. The statistical comparisons are accessed using the Kruskal-Wallis test with Dunn’s post-hoc analysis, and results are compared against a conventional average-base method using Jensen-Shannon distance. The results show that dynamic features of EEG signals – particularly in the theta, alpha, and beta bands – effectively distinguish Alzheimer’s patients from healthy individuals, while the Shannon entropy of signal dynamics in frontal region differentiates frontotemporal dementia patients across the theta to gamma bands. Moreover, changes in the occipital region detected by information rate, but not by traditional method, further highlight the importance of capturing temporal variability. The method also successfully distinguishes individuals with Mild Cognitive Impairment from healthy controls, which conventional analysis failed to achieve. These results suggest that analyzing the dynamics properties of the brain signals provides a more sensitive and informative approach for identifying and distinguishing various forms of dementia.Author summary: In this paper, we introduce a method called information rate to evaluate the dynamics of EEG signals. This method quantifies signals collectively within a dimensionless statistical space, whereas conventional methods tend to focus on either single signal or pairwise comparisons between signals. We compare information rate with an average-based approach, Jensen-Shannon distance, which samples the statistical space over time to enable pairwise comparisons between regions of interest across the EEG signals. To validate our findings, we performed Kruskal-Wallis and Dunn’s statistical tests. Our results indicate that the information rate successfully differentiates healthy participants from patients with cognitive disorder (Alzheimer’s, frontotemporal dementia, and Mild Cognitive Impairment). Furthermore, the dynamic analysis using information rate reveals distinctions between healthy and cognitively impaired groups at specific frequency bands in particular brain regions, a differences that the Jensen-Shannon distance does not capture.