A brain-computer interface (BCI) is a technology that enables direct communication between the brain and an external device or computer system. It allows individuals to interact with the device using only their thoughts, and holds immense potential for a wide range of applications in medicine, rehabilitation, and human augmentation. An electroencephalogram (EEG) and event-related potential (ERP)-based speller system is a type of BCI that allows users to spell words without using a physical keyboard, but instead by recording and interpreting brain signals under different stimulus presentation paradigms. Conventional non-adaptive paradigms treat each word selection independently, leading to a lengthy learning process. To improve the sampling efficiency, we cast the problem as a sequence of best-arm identification tasks in multi-armed bandits. Leveraging pre-trained large language models (LLMs), we utilize the prior knowledge learned from previous tasks to inform and facilitate subsequent tasks. To do so in a coherent way, we propose a sequential top-two Thompson sampling (STTS) algorithm under the fixed-confidence setting and the fixed-budget setting. We study the theoretical property of the proposed algorithm, and demonstrate its substantial empirical improvement through both synthetic data analysis as well as a P300 BCI speller simulator example.

翻译：中文摘要：脑机接口（BCI）是一种实现大脑与外部设备或计算机系统直接通信的技术。它允许个体仅通过思维与设备交互，在医学、康复及人类增强领域具有广泛的应用潜力。基于脑电图（EEG）和事件相关电位（ERP）的拼写系统是一种BCI技术，使用户无需物理键盘即可拼写单词，其核心通过记录和解读不同刺激范式下的脑信号实现。传统非自适应范式独立处理每次单词选择，导致学习过程冗长。为提高采样效率，本文将问题建模为多臂老虎机中的序列最佳臂识别任务。通过利用预训练大语言模型（LLMs）的先验知识，我们能够将先前任务的学习经验迁移至后续任务。为实现连贯的迁移学习，我们提出固定置信度设置与固定预算设置下的序列式Top-two Thompson采样（STTS）算法。本文研究了该算法的理论性质，并通过合成数据分析及P300 BCI拼写模拟器示例验证了其显著的实证改进效果。