Neural Spelling: A Spell-Based BCI System for Language Neural Decoding
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866917303854039040 |
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| author | Jiang, Xiaowei Zhou, Charles Duan, Yiqun Zhao, Ziyi Do, Thomas Lin, Chin-Teng |
| author_facet | Jiang, Xiaowei Zhou, Charles Duan, Yiqun Zhao, Ziyi Do, Thomas Lin, Chin-Teng |
| contents | Brain-computer interfaces (BCIs) present a promising avenue by translating neural activity directly into text, eliminating the need for physical actions. However, existing non-invasive 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 spell-based neural language decoding tasks. Our approach combines the ease of handwriting with the accessibility of EEG technology, utilizing advanced neural decoding algorithms and pre-trained large language models (LLMs) to translate EEG patterns into text with high accuracy. This system show how GenAI can improve the performance of typical spelling-based neural language decoding task, and addresses the limitations of previous methods, offering a scalable and user-friendly solution for individuals with communication impairments, thereby enhancing inclusive communication options. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_17489 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Neural Spelling: A Spell-Based BCI System for Language Neural Decoding Jiang, Xiaowei Zhou, Charles Duan, Yiqun Zhao, Ziyi Do, Thomas Lin, Chin-Teng Human-Computer Interaction Artificial Intelligence Brain-computer interfaces (BCIs) present a promising avenue by translating neural activity directly into text, eliminating the need for physical actions. However, existing non-invasive 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 spell-based neural language decoding tasks. Our approach combines the ease of handwriting with the accessibility of EEG technology, utilizing advanced neural decoding algorithms and pre-trained large language models (LLMs) to translate EEG patterns into text with high accuracy. This system show how GenAI can improve the performance of typical spelling-based neural language decoding task, and addresses the limitations of previous methods, offering a scalable and user-friendly solution for individuals with communication impairments, thereby enhancing inclusive communication options. |
| title | Neural Spelling: A Spell-Based BCI System for Language Neural Decoding |
| topic | Human-Computer Interaction Artificial Intelligence |
| url | https://arxiv.org/abs/2501.17489 |