EBFNet: Enhanced Brain Feature Network for Auditory Attention Detection

Le résumé fourni par la source

Abstract Auditory Attention Detection (AAD) approach seeks to identify the target speaker in multi-talker environments by analyzing electroencephalography (EEG) signals, addressing the cocktail party problem. However, existing EEG-based AAD methods overlook the spatial correlation patterns across different ranges among EEG channels and the collaborative capture of instantaneous and long-range features in temporal dynamics, which limits their ability to extract fine spatiotemporal features of brain activities. To address these issues, this paper proposes the Enhanced Brain Feature Network (EBFNet), which consists of the Spatial Feature Extraction (SFE) module, Temporal Sequence Modeling (TSM) module, and Attention Temporal-Spatial Fusion (ATF) module. The SFE employs multi-scale convolutions to capture spatial patterns across EEG channels. The TSE models instantaneous and sustained temporal patterns by processing long-range dependencies through global multi-head attention and capturing short-range changes with local convolutions. The ATF optimizes spatiotemporal features by weighting key channel-time interactions via attention gating, enhancing the relevance for attention decoding. Experimental results on the KUL dataset show that EBFNet increases classification accuracy by 8.0%, 1.7%, and 1.5% for 0.1-second, 1-second, and 2-second decision windows, while reducing trainable parameters by 50%.

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