Background: NMDA receptor encephalitis (NMDA-RE) is an autoimmune disorder caused by antibodies to the NR1-NR2B heterodimer of the NMDA receptor. Currently, disease status is tracked primarily by the presence of auto-antibodies in the cerebrospinal fluid (CSF) and serum. Using serological and CSF markers along with clinical parameters to track disease progress can be challenging since patient symptoms and disease progress can vary widely. Methods: EEGs were reviewed in a 31 year old male patient with proven NMDA-RE. EEG data were sampled from various times before and after diagnosis, as well as during various stages of treatment. All analyses were performed using Matlab (Mathworks). Results: We showed that using a simple 1/f model of spectral behaviour (Buzsaki and Draguhn, 2004), we could fit the power spectra of the raw data at various instances during routine EEGs. We have demonstrated that the values of specific fitting parameters vary in relationship to the patient’s clinical status across various stages of illness. Conclusions: The aim of this project was to explore the potential utility of EEG as a complement to the usual clinical metrics used in monitoring NMDA-RE. The analysis techniques presented here highlight the use of EEG as a practical, minimaly-invasive tool to monitor progress and potentially aid in clinical decision making.