A P300 ERP-based Brain-Computer Interface (BCI) speller is an assistive communication tool. It searches for the P300 event-related potential (ERP) elicited by target stimuli, distinguishing it from the neural responses to non-target stimuli embedded in electroencephalogram (EEG) signals. Conventional methods require a lengthy calibration procedure to construct the binary classifier, which reduced overall efficiency. Thus, we proposed a unified framework with minimum calibration effort such that, given a small amount of labeled calibration data, we employed an adaptive semi-supervised EM-GMM algorithm to update the binary classifier. We evaluated our method based on character-level prediction accuracy, information transfer rate (ITR), and BCI utility. We applied calibration on training data and reported results on testing data. Our results indicate that, out of 15 participants, 9 participants exceed the minimum character-level accuracy of 0.7 using either on our adaptive method or the benchmark, and 7 out of these 9 participants showed that our adaptive method performed better than the benchmark. The proposed semi-supervised learning framework provides a practical and efficient alternative to improve the overall spelling efficiency in the real-time BCI speller system, particularly in contexts with limited labeled data.

翻译：基于P300事件相关电位的脑机接口拼写系统是一种辅助通信工具。该系统通过检测目标刺激诱发的P300事件相关电位，并将其与脑电信号中非目标刺激引发的神经响应进行区分。传统方法需要冗长的校准程序来构建二分类器，这降低了整体效率。为此，我们提出了一种具有最小校准量的统一框架：在给定少量带标签校准数据的前提下，采用自适应半监督EM-GMM算法更新二分类器。我们从字符级预测准确率、信息传输率和脑机接口效用三个维度评估了该方法，并在训练数据上实施校准后报告了测试数据的结果。实验结果表明：在15名参与者中，有9名参与者使用本自适应方法或基准方法时字符级准确率超过0.7的最低阈值，且其中7名参与者的表现显示本自适应方法优于基准方法。所提出的半监督学习框架为提升实时脑机接口拼写系统的整体拼写效率提供了实用高效的解决方案，特别适用于带标签数据有限的场景。