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Recently, it was shown that auditory brain-computerinterface (BCI) classifications can be performed in real life environments. However, the need for initial training of existing supervised classifiers on large parts of that data discourages practical application. Reducing calibration time of BCI analyses would benefit short-term interactions. The current paper presents a carefully simulated study of P300 Event-Related-Potential (ERP) data to illustrate the performance of tensor decompositions for data-driven classification of the P300 effect. The aim of this study was to investigate whether coupling of a highand low-noise dataset can enhance data-driven clustering of the P300. Imposing structure and linking the decompositions of higher dimensional data arrays called tensors was hypothesized to increase the classification accuracy. For the highest noise dataset (SNR=0.60), we demonstrated that imposing a coupling to datasets with a lower noise level can significantly improve the extracted clusters to classify target from non-target trials to achieve equal accuracy to the widely used supervised regularized Linear-DiscriminantAnalysis (rLDA). We evaluated the performance of Canonical Polyadic Decomposition (CPD) and decomposition in multilinear rank Lr,Lr,1 terms (LL1). These structured models do not need a training phase or label information, although they require additional data. Finally, we illustrated the potential of the tensor approach for the analysis of simultaneous EEG recordings in which the trial mode is shared between subjects. Without a priori knowledge of the signal of interest, the tensor-based models successfully separated the two stimuli classes in the highest noise scenario up to 100% for coupling of five simulated subjects. These results highlight the benefits of exploiting structure in the stimuli and experimental setup (e.g., conditions or subjects).

Data-Driven Clustering of P300 Eeg Data Using Coupled Tensor Decompositions