Neuroanatomy-Informed Brain-Machine Hybrid Intelligence for Robust Acoustic Target Detection.

ABSTRACT: Sound target detection (STD) plays a critical role in modern acoustic sensing systems. However, existing automated STD methods show poor robustness and limited generalization, especially under low signal-to-noise ratio (SNR) conditions or when processing previously unencountered sound categories. To overcome these limitations, we first propose a brain-computer interface (BCI)-based STD method that utilizes neural responses to auditory stimuli. Our approach features the Triple-Region Spatiotemporal Dynamics Attention Network (Tri-SDANet), an electroencephalogram (EEG) decoding model incorporating neuroanatomical priors derived from EEG source analysis to enhance decoding accuracy and provide interpretability in complex auditory scenes. Recognizing the inherent limitations of stand-alone BCI

SUBMITTER: Shi J 

PROVIDER: S-EPMC12531490 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature