An Event-Related Potential (ERP)-based Brain-Computer Interface (BCI) Speller System assists people with disabilities to communicate by decoding electroencephalogram (EEG) signals. A P300-ERP embedded in EEG signals arises in response to a rare, but relevant event (target) among a series of irrelevant events (non-target). Different machine learning methods have constructed binary classifiers to detect target events, known as calibration. The existing calibration strategy uses data from participants themselves with lengthy training time. Participants feel bored and distracted, which causes biased P300 estimation and decreased prediction accuracy. To resolve this issue, we propose a Bayesian signal matching (BSM) framework to calibrate EEG signals from a new participant using data from source participants. BSM specifies the joint distribution of stimulus-specific EEG signals among source participants via a Bayesian hierarchical mixture model. We apply the inference strategy. If source and new participants are similar, they share the same set of model parameters; otherwise, they keep their own sets of model parameters; we predict on the testing data using parameters of the baseline cluster directly. Our hierarchical framework can be generalized to other base classifiers with parametric forms. We demonstrate the advantages of BSM using simulations and focus on the real data analysis among participants with neuro-degenerative diseases.

翻译：基于事件相关电位（ERP）的脑机接口（BCI）拼写系统通过解码脑电图（EEG）信号，协助残障人士进行交流。嵌入EEG信号中的P300-ERP响应于一系列无关事件（非目标）中出现的罕见但相关事件（目标）而产生。不同的机器学习方法已构建了用于检测目标事件的二分类器，此过程称为校准。现有的校准策略使用参与者自身的数据，需要冗长的训练时间。参与者感到无聊和分心，这会导致P300估计偏差和预测准确性下降。为解决此问题，我们提出了一个贝叶斯信号匹配（BSM）框架，利用源参与者的数据来校准新参与者的EEG信号。BSM通过贝叶斯分层混合模型，指定了源参与者之间特定于刺激的EEG信号的联合分布。我们应用推断策略：若源参与者与新参与者相似，则他们共享同一组模型参数；否则，他们保持各自的模型参数集；我们直接使用基线簇的参数对测试数据进行预测。我们的分层框架可以推广到其他具有参数形式的基础分类器。我们通过仿真实验展示了BSM的优势，并重点分析了患有神经退行性疾病的参与者的真实数据。