文献类型：期刊文献

英文题名：Subject-independent auditory spatial attention detection based on brain topology modeling and feature distribution alignment

作者：Niu, Yixiang[1];Chen, Ning[1];Zhu, Hongqing[1];Li, Guangqiang[1];Chen, Yibo[1]

机构：[1]East China Univ Sci & Technol, Sch Informat Sci & Engn, Shanghai 200237, Peoples R China

年份：2024

卷号：453

外文期刊名：HEARING RESEARCH

收录：;WOS:【SCI-EXPANDED(收录号:WOS:001312294100001)】；

基金：Funding This work was supported by the National Natural Science Foundation of China [grant numbers 61771196, 61872143] .

语种：英文

外文关键词：Auditory spatial attention detection; Electroencephalogram; Domain generalization; Brain functional connectivity; Graph neural network

摘要：Auditory spatial attention detection (ASAD) seeks to determine which speaker in a surround sound field a listener is focusing on based on the one's brain biosignals. Although existing studies have achieved ASAD from a single- trial electroencephalogram (EEG), the huge inter-subject variability makes them generally perform poorly in cross-subject scenarios. Besides, most ASAD methods do not take full advantage of topological relationships between EEG channels, which are crucial for high-quality ASAD. Recently, some advanced studies have introduced graph-based brain topology modeling into ASAD, but how to calculate edge weights in a graph to better capture actual brain connectivity is worthy of further investigation. To address these issues, we propose a new ASAD method in this paper. First, we model a multi-channel EEG segment as a graph, where differential entropy serves as the node feature, and a static adjacency matrix is generated based on inter-channel mutual information to quantify brain functional connectivity. Then, different subjects' EEG graphs are encoded into a shared embedding space through a total variation graph neural network. Meanwhile, feature distribution alignment based on multi-kernel maximum mean discrepancy is adopted to learn subject-invariant patterns. Note that we align EEG embeddings of different subjects to reference distributions rather than align them to each other for the purpose of privacy preservation. A series of experiments on open datasets demonstrate that the proposed model outperforms state-of-the-art ASAD models in cross-subject scenarios with relatively low computational complexity, and feature distribution alignment improves the generalizability of the proposed model to a new subject.