MindChat: Enhancing BCI Spelling with Large Language Models in Realistic Scenarios
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| Main Authors: | , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866918106974126080 |
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| author | Wang, JIaheng Zhong, Yucun Huang, Chengjie Yao, Lin |
| author_facet | Wang, JIaheng Zhong, Yucun Huang, Chengjie Yao, Lin |
| contents | Brain-computer interface (BCI) spellers can render a new communication channel independent of peripheral nervous system, which are especially valuable for patients with severe motor disabilities. However, current BCI spellers often require users to type intended utterances letter-by-letter while spelling errors grow proportionally due to inaccurate electroencephalogram (EEG) decoding, largely impeding the efficiency and usability of BCIs in real-world communication. In this paper, we present MindChat, a large language model (LLM)-assisted BCI speller to enhance BCI spelling efficiency by reducing users' manual keystrokes. Building upon prompt engineering, we prompt LLMs (GPT-4o) to continuously suggest context-aware word and sentence completions/predictions during spelling. Online copy-spelling experiments encompassing four dialogue scenarios demonstrate that MindChat saves more than 62\% keystrokes and over 32\% spelling time compared with traditional BCI spellers. We envision high-speed BCI spellers enhanced by LLMs will potentially lead to truly practical applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_21435 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MindChat: Enhancing BCI Spelling with Large Language Models in Realistic Scenarios Wang, JIaheng Zhong, Yucun Huang, Chengjie Yao, Lin Human-Computer Interaction Brain-computer interface (BCI) spellers can render a new communication channel independent of peripheral nervous system, which are especially valuable for patients with severe motor disabilities. However, current BCI spellers often require users to type intended utterances letter-by-letter while spelling errors grow proportionally due to inaccurate electroencephalogram (EEG) decoding, largely impeding the efficiency and usability of BCIs in real-world communication. In this paper, we present MindChat, a large language model (LLM)-assisted BCI speller to enhance BCI spelling efficiency by reducing users' manual keystrokes. Building upon prompt engineering, we prompt LLMs (GPT-4o) to continuously suggest context-aware word and sentence completions/predictions during spelling. Online copy-spelling experiments encompassing four dialogue scenarios demonstrate that MindChat saves more than 62\% keystrokes and over 32\% spelling time compared with traditional BCI spellers. We envision high-speed BCI spellers enhanced by LLMs will potentially lead to truly practical applications. |
| title | MindChat: Enhancing BCI Spelling with Large Language Models in Realistic Scenarios |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2507.21435 |