P300 speller BCIs allow users to compose sentences by selecting target keys on a GUI through the detection of P300 component in their EEG signals following visual stimuli. Most P300 speller BCIs require users to spell words letter by letter, or the first few initial letters, resulting in high keystroke demands that increase time, cognitive load, and fatigue. This highlights the need for more efficient, user-friendly methods for faster sentence composition. In this work, we introduce ChatBCI, a P300 speller BCI that leverages the zero-shot learning capabilities of large language models (LLMs) to suggest words from user-spelled initial letters or predict the subsequent word(s), reducing keystrokes and accelerating sentence composition. ChatBCI retrieves word suggestions through remote queries to the GPT-3.5 API. A new GUI, displaying GPT-3.5 word suggestions as extra keys is designed. SWLDA is used for the P300 classification. Seven subjects completed two online spelling tasks: 1) copy-spelling a self-composed sentence using ChatBCI, and 2) improvising a sentence using ChatBCI's word suggestions. Results demonstrate that in Task 1, on average, ChatBCI outperforms letter-by-letter BCI spellers, reducing time and keystrokes by 62.14% and 53.22%, respectively, and increasing information transfer rate by 198.96%. In Task 2, ChatBCI achieves 80.68% keystroke savings and a record 8.53 characters/min for typing speed. Overall, ChatBCI, by employing remote LLM queries, enhances sentence composition in realistic scenarios, significantly outperforming traditional spellers without requiring local model training or storage. ChatBCI's (multi-) word predictions, combined with its new GUI, pave the way for developing next-generation speller BCIs that are efficient and effective for real-time communication, especially for users with communication and motor disabilities.

翻译：P300拼写脑机接口允许用户通过视觉刺激后在其脑电信号中检测P300成分，从而在图形用户界面上选择目标按键来构建句子。大多数P300拼写脑机接口要求用户逐个字母拼写单词，或仅拼写前几个起始字母，这导致高击键需求，从而增加了时间、认知负荷和疲劳。这凸显了对更高效、用户友好的快速句子构建方法的需求。在本工作中，我们介绍了ChatBCI，这是一种P300拼写脑机接口，它利用大型语言模型的零样本学习能力，根据用户拼写的起始字母建议单词或预测后续单词，从而减少击键次数并加速句子构建。ChatBCI通过远程查询GPT-3.5 API获取单词建议。我们设计了一个新的图形用户界面，将GPT-3.5的单词建议显示为额外按键。P300分类采用SWLDA方法。七名受试者完成了两项在线拼写任务：1）使用ChatBCI复制拼写一个自创句子；2）利用ChatBCI的单词建议即兴构建一个句子。结果表明，在任务1中，ChatBCI平均表现优于逐字母拼写的脑机接口，分别将时间和击键次数减少了62.14%和53.22%，并将信息传输率提高了198.96%。在任务2中，ChatBCI实现了80.68%的击键节省，并达到了8.53字符/分钟的创纪录打字速度。总体而言，ChatBCI通过采用远程大型语言模型查询，在真实场景中增强了句子构建能力，显著优于传统拼写器，且无需本地模型训练或存储。ChatBCI的（多）单词预测功能，结合其新的图形用户界面，为开发高效、有效的下一代拼写脑机接口铺平了道路，尤其适用于存在沟通和运动障碍的用户进行实时交流。