Electroencephalography (EEG) is a non-invasive, cost-effective neurophysiological tool with high temporal resolution, widely used in the study and clinical evaluation of dementia. EEG reflects abnormal neuronal activity and loss of functional connectivity during neurodegeneration. Quantitative EEG (qEEG) and coherence analysis can aid in distinguishing various types of dementia, such as Alzheimer’s disease (AD), dementia with Lewy bodies (DLB), frontotemporal lobar degeneration (FTLD), and vascular dementia (VaD). In AD, EEG typically shows generalized slowing, reduced alpha activity, and subclinical epileptiform discharges (SEDs), which have been associated with faster cognitive decline and more pronounced behavioral and psychological symptoms (BPSD). Epileptic activity is also more frequent in AD, potentially triggered by amyloid-β and tau-related hyperexcitability. In DLB, prominent slow-wave activity with periodic fluctuations in the pre-alpha/theta frequency range is considered a supportive biomarker. In Creutzfeldt-Jakob disease (CJD), periodic sharp wave complexes (PSWCs) are characteristic, especially in the middle to late stages of the disease. The integration of EEG with other biomarkers—enhanced by artificial intelligence and deep learning—has shown promise in improving the accuracy of early diagnosis, monitoring disease progression, and tailoring individualized therapeutic interventions in dementia care.