Feature interpretability in BCIs: exploring the role of network lateralization

TL;DR

This paper evaluates the interpretability of brain network lateralization features in EEG-based BCIs, comparing them with traditional methods, and finds they offer neurophysiologically meaningful insights despite not surpassing existing classifiers in accuracy.

Contribution

It introduces network lateralization metrics as interpretable features for motor imagery BCIs and benchmarks their performance against standard techniques.

Findings

Network lateralization shows stronger contralateral lateralization in sensorimotor areas.

Lateralization features are biologically plausible and provide interpretability.

Performance is competitive with PSD but less accurate than CSP and Riemannian methods.

Abstract

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.