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Auditory attention detection (AAD) aims to identify the direction of theattended speaker in multi-speaker environments from brain signals, such asElectroencephalography (EEG) signals. However, existing EEG-based AAD methodsoverlook the spatio-temporal dependencies of EEG signals, limiting theirdecoding and generalization abilities. To address these issues, this paperproposes a Lightweight Spatio-Temporal Enhancement Nested Network (ListenNet)for AAD. The ListenNet has three key components: Spatio-temporal DependencyEncoder (STDE), Multi-scale Temporal Enhancement (MSTE), and Cross-NestedAttention (CNA). The STDE reconstructs dependencies between consecutive timewindows across channels, improving the robustness of dynamic patternextraction. The MSTE captures temporal features at multiple scales to representboth fine-grained and long-range temporal patterns. In addition, the CNAintegrates hierarchical features more effectively through novel dynamicattention mechanisms to capture deep spatio-temporal correlations. Experimentalresults on three public datasets demonstrate the superiority of ListenNet overstate-of-the-art methods in both subject-dependent and challengingsubject-independent settings, while reducing the trainable parameter count byapproximately 7 times. Code is availableat:https://github.com/fchest/ListenNet.

ListenNet: A Lightweight Spatio-Temporal Enhancement Nested Network for Auditory Attention Detection