Dementia, including Alzheimer’s and frontotemporal dementia, often causes overlapping symptoms, making diagnosis challenging. Traditional imaging is costly and slow, while EEG offers a cheaper, portable option–but interpreting signals has been difficult. FAU researchers have developed a deep learning model that analyzes EEG brain activity to accurately detect both type and severity of dementia. This AI-driven approach identifies key brainwave patterns, enabling faster, noninvasive, and precise monitoring of disease progression, transforming dementia diagnosis and care.
