User Modeling Using EEG Signals: A Systematic Literature Review

Electroencephalography (EEG) has emerged as a critical tool for user modeling in brain-computer interfaces (BCIs), offering unique perspectives into cognitive processes and user behavior. This systematic review explores how EEG signals have been used to model various facets of user behavior, from attention and cognitive load to emotional responses and personality. We analyze transdisciplinary studies incorporating EEG in BCIs, This review highlights advanced signal processing techniques. Additionally, it examines machine learning algorithms that enable sophisticated interpretation of brain signals in real-time. In addition, we observe how these method-ologies are applied in contexts ranging from collaborative gaming to device control in home and clinical settings. Through the integration of key findings, this review identifies trends in the accurate modeling of user behavior and performance of BCIs, as well as the challenges inherent in working with interindividual variability and neurophysiological signals.