Deep Learning for EEG‐Based Alzheimer’s Disease Diagnosis

Electroencephalography (EEG) could be a powerful tool to diagnose Alzheimer’s Disease (AD) because it is pervasive, non‐invasive, and cost‐effective [1]. However, EEG‐based AD detection has suffered from poor performance and low generalizability due to the noisy nature of the signal and high inter‐patient variability. Here, we show that emerging deep learning techniques can alleviate these challenges and pave the way for the clinical use of EEG in AD detection.