Clinicians have identified distinctive electroencephalography signatures—specifically extreme delta brush patterns and generalized rhythmic delta activity—that can help detect anti-NMDA receptor encephalitis, a dangerous autoimmune condition where the body attacks brain receptors critical for memory and cognition. The research also highlights circulating microRNA molecules as complementary biomarkers for both diagnosis and prognosis assessment. This dual-biomarker approach addresses a significant clinical challenge, as anti-NMDA receptor encephalitis often mimics psychiatric disorders in early stages, leading to delayed treatment and potentially irreversible neurological damage. The condition predominantly affects young women and can progress rapidly from behavioral changes to seizures, coma, and death without prompt immunotherapy. Traditional diagnosis relies heavily on cerebrospinal fluid antibody detection, which requires invasive lumbar puncture and specialized laboratory capabilities not universally available. The integration of EEG pattern recognition with blood-based microRNA profiling could democratize early detection, particularly in resource-limited settings where rapid antibody testing isn't feasible. However, the specificity of these biomarkers across diverse patient populations and their performance in distinguishing anti-NMDA receptor encephalitis from other autoimmune encephalitides remains to be validated through larger prospective studies.