用少量校准数据实现P300脑机拼写系统自适应优化，提升实时效率。
Adaptive Semi-Supervised Training of P300 ERP-BCI Speller System with Minimum Calibration Effort
- 基于少量标签数据，采用自适应半监督算法动态更新分类器
- 15人中9人准确率超0.7，7人性能优于传统方法
- 适合资源受限场景，降低脑机接口使用门槛
基于P300事件相关电位的脑机接口拼写系统是一种辅助沟通工具，通过识别目标刺激诱发的P300 ERP信号，区分其与非目标刺激的脑电信号。传统方法需长时间校准以构建二分类器，影响整体效率。为此，我们提出一种最小化校准需求的统一框架：仅需少量标注校准数据，便采用自适应半监督EM-GMM算法更新分类器。基于字符级预测准确率、信息传输率（ITR）和脑机接口实用性进行评估。在训练数据上校准，于测试数据上报告结果。结果显示，在15名参与者中，9人字符级准确率超过0.7，其中7人采用本方法表现优于基准。所提半监督学习框架为真实场景下的脑机拼写系统提供了高效实用的替代方案，尤其适用于标注数据有限的情况。
原文摘要 · Abstract (English)
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.
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