  Brain-computer interfaces (BCIs) enable users to interact with the external
world using brain activity. Despite their potential in neuroscience and
industry, BCI performance remains inconsistent in noninvasive applications,
often prioritizing algorithms that achieve high classification accuracies while
masking the neural mechanisms driving that performance. In this study, we
investigated the interpretability of features derived from brain network
lateralization, benchmarking against widely used techniques like power spectrum
density (PSD), common spatial pattern (CSP), and Riemannian geometry. We
focused on the spatial distribution of the functional connectivity within and
between hemispheres during motor imagery tasks, introducing network-based
metrics such as integration and segregation. Evaluating these metrics across
multiple EEG-based BCI datasets, our findings reveal that network
lateralization offers neurophysiological plausible insights, characterized by
stronger lateralization in sensorimotor and frontal areas contralateral to
imagined movements. While these lateralization features did not outperform CSP
and Riemannian geometry in terms of classification accuracy, they demonstrated
competitive performance against PSD alone and provided biologically relevant
interpretation. This study underscores the potential of brain network
lateralization as a new feature to be integrated in motor imagery-based BCIs
for enhancing the interpretability of noninvasive applications.
