文献类型：期刊文献

英文题名：Detecting the locus of auditory attention based on the spectro-spatial-temporal analysis of EEG

作者：Jiang, Yifan[1];Chen, Ning[1];Jin, Jing[1]

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

年份：2022

卷号：19

期号：5

外文期刊名：JOURNAL OF NEURAL ENGINEERING

收录：;EI(收录号：20224413038401);WOS:【SCI-EXPANDED(收录号:WOS:000868887600001)】；

基金：This work was supported by the National Natural Science Foundation of China [Grant Number 61771196]. I would like to thank Siqi Cai for providing the code of the SSF-CNN model in [23]. The authors would like to thank the anonymous reviewers and the associate editor for their insightful comments that significantly improved the quality of this paper.

语种：英文

外文关键词：auditory attention decoding (AAD); EEG; differential entropy (DE); ConvLSTM; spectro-spatial-temporal feature

摘要：Objective. Auditory attention decoding (AAD) determines which speaker the listener is focusing on by analyzing his/her EEG. Convolutional neural network (CNN) was adopted to extract spectro-spatial-feature (SSF) from short-time-interval of EEG to detect auditory spatial attention without stimuli. However, the following factors are not considered in SSF-CNN scheme. (a) Single-band frequency analysis cannot represent the EEG pattern precisely. (b) The power cannot represent the EEG feature related to the dynamic patterns of the attended auditory stimulus. (c) The temporal feature of EEG representing the relationship between EEG and attended stimulus is not extracted. To solve these problems, SSF-CNN scheme was modified. Approach. (a) Multiple-frequency bands, but not a single alpha frequency band, of EEG, were analyzed to represent the EEG pattern more precisely. (b) Differential entropy, but not power, was extracted from each frequency band to represent the disorder degree of EEG, which was related to the dynamic patterns of the attended auditory stimulus. (c) CNN and convolutional-long-short-term-memory (ConvLSTM) were combined to extract spectro-spatial-temporal features from the 3D descriptor sequence constructed based on the topographical activity maps of multiple-frequency bands. Main results. Experimental results on KUL, DTU, and PKU with 0.1 s, 1 s, 2 s, and 5 s decision windows demonstrated that: (a) The proposed model outperformed SSF-CNN and state-of-the-art AAD models. Specifically, when the auditory stimulus was unavailable, AAD accuracy could be enhanced by at least 3.25%, 3.96% and 5.08% on KUL, DTU, and PKU, respectively, compared with the baselines. And, on KUL, the longer decision window corresponded to lower enhancement, while on both DTU and PKU, the longer decision window corresponded to higher enhancement, except for two cases when decision window length was 2 s on PKU or 5 s on DTU. (b) Each modification contributed to the performance enhancement. Significance. DE feature, multi-band frequency analysis, and ConvLSTM-based temporal analysis help to enhance AAD accuracy.