Abstract

This research addresses the challenge of improving SSVEP-based brain-computer interface (BCI) spellers through advanced data augmentation techniques and language model integration. The study focuses on enhancing the accuracy and reliability of communication systems for individuals with motor disabilities, particularly in scenarios where brain signals may be unclear or inconsistent.

The proposed methodology combines sophisticated data augmentation strategies with state-of-the-art language models to boost the performance of SSVEP BCI spellers. By leveraging contextual understanding and predictive capabilities, the system can provide more intuitive and reliable communication interfaces. The research demonstrates significant improvements in spelling accuracy and user experience, contributing to the advancement of assistive technologies for individuals with communication impairments.