Alzheimer’s disease (AD) is a common progressive neurodegenerative disease characterized prominently by recent memory impairment, often accompanied by psychiatric and behavioral symptoms as well as varying degrees of functional impairment in daily living abilities. The insidious onset and gradual progression of AD pose substantial challenges to early clinical diagnosis. Under the amyloid β -tau-neurodegeneration (ATN) diagnostic framework proposed by the National Institute on Aging and Alzheimer’s Association (NIAAA) in 2024, the biological diagnosis of AD mainly relies on cerebrospinal fluid examination and positron emission tomography (PET), but the former is invasive and the latter is expensive and equipment dependent, severely limiting their clinical popularity. Therefore, exploring a non-invasive, economical, and dynamic functional biomarker that can reflect neurodegeneration (i. e., “N”) has become an urgent problem in this field. Electroencephalogram (EEG) has shown unique advantages in the study of cognitive impairment due to its low cost, non-invasive nature, high temporal resolution, and good reproducibility. Its signals are highly sensitive to synaptic dysfunction and abnormal network oscillations, and can capture real-time brain network dysfunction and directly map the electrophysiological basis of neurodegeneration. In recent years, the combined application of EEG with multimodal neuroimaging techniques, such as transcranial magnetic stimulation (TMS), functional magnetic resonance imaging (fMRI), and functional nearinfrared spectroscopy (fNIRS), has developed rapidly, providing rich functional information for early identification of AD from multiple dimensions, including cortical excitability, causal network connectivity, oscillation blood flow coupling, and neurovascular coupling. This article systematically reviews the application progress of resting-state EEG spectrum and microstate analysis, TMS-EEG, EEG-fMRI, and EEG-fNIRS in AD and mild cognitive impairment (MCI), focusing on how these non-invasive techniques complement and integrate with existing ATN diagnostic frameworks. At the same time, it analyzes the limitations faced by current research in sample size, standardization, and clinical translation, and looks forward to future technological optimization, multi-center validation, and potential pathway for intergrating the ATN diagnostic network.