Auditory Attention Decoding from Ear-EEG Signals: A Dataset with Dynamic Attention Switching and Rigorous Cross-Validation

Abstract

Recent promising results in auditory attention decoding (AAD) using scalp electroencephalography (EEG) have motivated the exploration of cEEGrid, a flexible and portable ear-EEG system. While prior cEEGrid-based studies have confirmed the feasibility of AAD, they often neglect the dynamic nature of attentional states in real-world contexts. To address this gap, a novel cEEGrid dataset featuring three concurrent speakers distributed across three of five distinct spatial locations is introduced. The novel dataset is designed to probe attentional tracking and switching in realistic scenarios. Nested leave-one-out validation-an approach more rigorous than conventional single-loop leave-one-out validation-is employed to reduce biases stemming from EEG's intricate temporal dynamics. Four rule-based models are evaluated: Wiener filter (WF), canonical component analysis (CCA), common spatial pattern (CSP) and Riemannian Geometry-based classifier (RGC). With a 30-second decision window, WF and CCA models achieve decoding accuracies of 41.5% and 41.4%, respectively, while CSP and RGC models yield 37.8% and 37.6% accuracies using a 10-second window. Notably, both WF and CCA successfully track attentional state switches across all experimental tasks. Additionally, higher decoding accuracies are observed for electrodes positioned at the upper cEEGrid layout and near the listener's right ear. These findings underscore the utility of dynamic, ecologically valid paradigms and rigorous validation in advancing AAD research with cEEGrid.