Tensor decomposition methods constitute an alternative way to analyze multi-dimensional data, with advantages with respect to classical linear techniques such as PCA (Principal Component Analysis). This is especially useful in the context of phase-amplitude coupling data, where these are computed for different band combinations for each channel and for each subject. Unlike PCA, which assumes that the data matrix is linear and typically focuses on variance maximization, tensor decomposition methods can handle multi-dimensional data (e.g., EEG signals across different frequency bands and channels) in a more flexible way by decomposing it into components that are not only spatially but also temporally and spectrally meaningful. As a result, it enables to model interactions across multiple modes, allowing a more accurate representation of the brain activity by capturing non-linear patterns. Moreover, it provides a more interpretable, robust and unique solution. In this work, we propose a method to study cerebral lateralization by means of tensor decomposition using the PARAFAC (Parallel Factor Analysis) model to identify spatial patterns along with bands contribution. This provides insights into hemispheric differences in cognitive processing, being able to detect abnormal patterns linked to language impairments.