Neural Spelling: A Spell-Based BCI System for Language Neural Decoding

Abstract--Brain-computer interfaces (BCIs) present a promising avenue by translating neural activity directly into text, eliminating the need for physical actions. However, existing noninvasive BCI systems have not successfully covered the entire alphabet, limiting their practicality. In this paper, we propose a novel non-invasive EEG-based BCI system with Curriculum-based Neural Spelling Framework, which recognizes all 26 alphabet letters by decoding neural signals associated with handwriting first, and then apply a Generative AI (GenAI) to enhance spellbased neural language decoding tasks. However, the invasive nature, high cost, and ethical concerns limit their I. RAIN-COMPUTER interfaces (BCIs) have emerged as a pivotal area of research within human-computer interaction accessible alternative. These systems are less obtrusive and (HCI), distinguished by their capacity to seamlessly more cost-effective, broadening potential user demographics [8]. Pioneering Despite the challenges of signal noise and the extensive training studies such as those by Guo et al.[1], Chen et al.[2], Cao et required for users, recent studies have demonstrated EEG's al.[3], and Lin et al.[4] underscore BCIs' role in advancing potential in effective language decoding [9, 10]. These interfaces create direct With the rise of Generative AI (GenAI), the integration of communication pathways that are especially beneficial for large language models (LLMs) into BCI research has opened individuals with limited speech or motor functions.