<p>Humans exhibit a remarkable ability to selectively focus on auditory stimuli in multi-speaker environments, such as cocktail parties. The Auditory Attention Detection (AAD) method aims to identify the conversation that a listener is attending to through the analysis of neural signals, particularly utilizing electroencephalography (EEG) data. However, current methodologies in this domain encounter several significant limitations. While many existing AAD methods use additional information–like spatial or frequency features–to improve decoding accuracy, they often miss the relationships between signals from different EEG channels. To address these shortcomings, this paper introduces a novel hybrid channel attention network for AAD. Our approach is the first to integrate spatial-temporal filtering, dynamic multi-scale feature fusion, and efficient cross-channel attention into a single unified architecture, enabling it to capture complex neural patterns of attention that previous methods overlooked. Our proposed network first extracts spatial-temporal features from raw EEG signals employing a dedicated spatial-temporal feature extraction module. The extracted features are then processed by a module that combines information across different time scales and uses an attention mechanism to identify important relationships between EEG channels. Experimental results demonstrate that our network achieves superior classification performance compared to baseline methods, particularly under conditions with short decision windows. Notably, while maintaining exceptional accuracy, the proposed architecture significantly reduces model parameters.</p>