Emotion analysis in mental health has evolved from lexicon-based systems to large language models (LLMs) capable of contextual affect inference, severity estimation, and empathic dialogue generation, reflecting advances in NLP and the recognition that language is a rich proxy for psychological state. This mini review synthesizes LLM applications in mental health emotion analysis, characterizing methodological trends, identifying strengths and limitations, and highlighting critical gaps in benchmarking, clinical validation, and governance. A structured PRISMA-informed search across six databases (2017--2025) using three Boolean keyword clusters yielded 44 studies after two-stage independent s... More >

Resting-state EEG offers a low-cost, non-invasive biomarker for Alzheimer's and Parkinson's diseases, yet most deep learning models fail to generalize to new patients due to subject-dependent evaluation protocols, isolated frequency-band processing, and the neglect of cross-frequency interactions. We propose CFGT-Net, a Cross-Frequency Graph-Transformer Network. For each EEG epoch, five canonical bands are processed by a graph attention encoder on a learnable phase-lag index adjacency to produce spatial embeddings. A cross-frequency coupling (CFC) attention models band interactions, a temporal transformer tracks their evolution across epochs, and a correlation-alignment loss enforces subject... More >