Cerebral Lateralization Assessment: An Explainable Deep Learning Approach With Channel Attention Mechanism

In recent years, cross-frequency coupling (CFC) has emerged as a valuable tool in the study of a wide range of cognitive processes due to the strong evidence of its functional role in neural computation and communication. CFC computed from electroencephalography (EEG) signals provides powerful information for detecting certain neurological conditions associated with atypical cerebral lateralization. The use of deep learning (DL) in this context offers several advantages, including improved scalability and adaptability to individual variability. However, it presents several significant challenges related to the limited availability of labelled samples and the high-dimensional and noisy nature of EEG data, which can lead to overfitting, poor generalization, and temporal and spatial variability between subjects. In this work, we propose a novel deep learning approach to reveal lateralization patterns based on inter-hemispheric functional differences via CFC. To overcome the challenges associated to the use of DL in this context, we propose the use of synthetic signals for pre-training the neural network that computes a specific type of CFC, phase-amplitude coupling (PAC), and a symmetric architecture for evaluating inter-hemispheric differences. Finally, our model incorporates a custom attention layer designed to learn the most relevant information across different EEG channels and its relative importance, further enhancing its ability to detect subtle hemispheric differences and providing the necessary explainability for clinical applications. The results demonstrate a good classification performance (AUC up to 0.85) in assessing lateralization, providing explainable insights into the mechanisms of the disorder. This may aid in early detection and provide a better understanding of the neural basis associated with this condition.