Canonical Polyadic Decomposition with Assistant Information for Brain Computer Interface

Canonical Polyadic Decomposition is an important method for multi-way array analysis, and has been successfully applied to a variety of signals. It can be seen as an unsupervised method for feature extraction in a classification problem. After that, a classifier, such as support vector machine, is utilized to classify those features. In this manner, classification task is achieved by two isolated steps. We proposed supervised Canonical Polyadic Decomposition by directly incorporating auxiliary label information during decomposition, with which a classification task can be achieved without an extra step of classifier training. The proposed method integrates decomposition and classifier learning together, so it reduces procedure of classification task compared with that of separated decomposition and classification. In order to evaluate the performance of the proposed method, three different kinds of signals, synthetic signal, EEG signal, and MEG signal, are used. The results based on evaluations of synthetic and real signals demonstrated the proposed method is effective and efficient.