Modeling multidimensional data using tensor models, in particular through the Canonical Polyadic (CP) model, can be found in large numbers of timely and important signal based applications. However, the computational complexity in the case of high-order and large-scale tensors remains a challenge that prevents the implementation of the CP model in practice. While some algorithms, in the literature, deal with large-scale problems, others target high-order tensors. Nevertheless, these algorithms encounter major issues when both problems are present. In this paper, we propose a parallelizable strategy, based on the tensor network theory, to deal simultaneously with both high-order and large-scale problems. We show the usefulness of the proposed strategy in reducing the computational time on a realistic electroencephalography data set.