Simulated online typing performance in a cBCI using different language models

Document Type

Conference Proceeding

Publication Date

1-1-2026

Department

Department of Mechanical Engineering-Engineering Mechanics

Abstract

Communication Brain-Computer Interfaces (cBCIs) represent a crucial technological advancement for individuals with severe motor disabilities as they offer a direct pathway to express their thoughts and needs without physical movement. These systems commonly leverage the P300 ERP, a distinct neural response approximately 300-500ms after a novel stimulus. Language modeling presents a promising approach to enhancing the performance and usability of cBCIs. However, integrating language models with cBCI systems presents unique challenges, including balancing model complexity with real-time processing requirements and optimizing system performance parameters. This study utilizes simulations of online cBCI data to investigate the impact of different language models on typing rate and accuracy.

Publication Title

NSF Public Access Repository (PAR)

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