Evaluating brain electroencephalogram signal dynamics across cognitive disorders using information geometry

Dementia, including Alzheimer’s disease and frontotemporal dementia, is a progres-
sive 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 infor- mation geometry method known as information rate, which captures the evolving pat- terns 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 distribu- tion of information rate are studied through the statistical moments (such as mean, vari- ance, 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 frontotem- poral dementia patients across the theta to gamma bands. Moreover, changes in the occipital region detected by information rate, but not by traditional method, further high- light the importance of capturing temporal variability. The method also successfully dis- tinguishes individuals with Mild Cognitive Impairment from healthy controls, which con- ventional 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.