Predicting Brain Computer Interface Gaming Performance Using Motor Imagery Electroencephalography and Deep Learning

Abstract

Early estimation of user capability remains a central requirement for adaptive brain computer interface gaming systems and directly affects usability, engagement, and training efficiency. Our research addresses the problem of forecasting multi-level control performance before gameplay by analyzing pre task motor imagery electroencephalography recorded with consumer grade devices. The scope focuses on supervised performance prediction across low medium and high skill groups derived from objective three dimensional gameplay scores. Time frequency representations derived from preprocessed neural signals were learned by a compact deep learning classifier trained on balanced datasets from two independent headsets. Evidence was drawn from epoch level evaluation and cross subject validation with subject wise voting, supported by statistical confidence analysis and visualization of learned spectral patterns. The results show high discrimination accuracy at the sample level and stable generalization across unseen participants, with higher performance observed for devices offering improved spatial sampling. Findings indicate early motor imagery responses encode information linked to later control proficiency. The contribution extends prior motor imagery decoding work toward predictive user profiling based on task related neural activity rather than command accuracy. Practical implications include personalized difficulty adaptation and reduced calibration effort. Future work should examine larger cohorts, hybrid spectral spatial representations, transfer learning across hardware, and real time deployment within adaptive neurogaming environments.