Higher-order PLS for classification of ERPs with application to BCIs

The EEG signals recorded during Brain Computer Interfaces (BCIs) are naturally represented by multi-way arrays in spatial, temporal, and frequency domains. In order to effectively extract the underlying components from brain activities which correspond to the specific mental state, we propose the higher-order PLS approach to find the latent variables related to the target labels and then make classification based on latent variables. To this end, the low-dimensional latent space can be optimized by using the higher-order SVD on a cross-product tensor, and the latent variables are considered as shared components between observed data and target output. The EEG signals recorded under the P300-type affective BCI paradigm were used to demonstrate the effectiveness of our new approach.