Bayesian Signal Matching for Transfer Learning in ERP-Based Brain Computer Interface

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. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.基于事件相关电位（Event-Related Potential, ERP）的脑机接口（Brain-Computer Interface, BCI）拼写系统，通过解码脑电图（Electroencephalogram, EEG）信号，帮助残障人士实现沟通。嵌入于EEG信号中的P300-ERP，会在一系列无关事件（非靶事件）中，对罕见但相关的事件（靶事件）产生响应。多种机器学习方法已构建二元分类器以检测靶事件，该过程被称为校准。现有校准策略依赖受试者自身的数据，训练时长过长，易导致受试者产生厌烦与注意力分散问题，进而造成P300电位估计偏倚，降低预测准确率。为解决该问题，本文提出贝叶斯信号匹配（Bayesian Signal Matching, BSM）框架，利用源受试者的数据对新受试者的EEG信号进行校准。BSM通过贝叶斯分层混合模型，对源受试者中刺激特异性EEG信号的联合分布进行建模。本文采用的推理策略为：若源受试者与新受试者具有相似性，则二者共享同一组模型参数；反之则各自保留专属的模型参数集，此时可直接基于基线簇的参数对测试数据进行预测。该分层框架可推广至其他具备参数化形式的基础分类器。本文通过仿真实验验证了BSM的优势，并聚焦于神经退行性疾病受试者的真实数据分析。本文的补充材料可在线获取，其中包含可复现研究的标准化材料说明。