An Event-Related Potential (ERP)-based Brain-Computer Interface (BCI) Speller System assists people with disabilities 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. Existing calibration strategy only uses data from participants themselves with lengthy training time, causing biased P300 estimation and decreasing prediction accuracy. To resolve this issue, we propose a Bayesian signal matching (BSM) framework for calibrating the 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 clear likelihood specifications. We demonstrate the advantages of BSM using simulations and focus on the real data analysis among participants with neuro-degenerative diseases.

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