Electroencephalography (EEG) plays a vital role in detecting how brain
responses to different stimulus. In this paper, we propose a novel Shallow-Deep
Attention-based Network (SDANet) to classify the correct auditory stimulus
evoking the EEG signal. It adopts the Attention-based Correlation Module (ACM)
to discover the connection between auditory speech and EEG from global aspect,
and the Shallow-Deep Similarity Classification Module (SDSCM) to decide the
classification result via the embeddings learned from the shallow and deep
layers. Moreover, various training strategies and data augmentation are used to
boost the model robustness. Experiments are conducted on the dataset provided
by Auditory EEG challenge (ICASSP Signal Processing Grand Challenge 2023).
Results show that the proposed model has a significant gain over the baseline
on the match-mismatch track.

本篇论文提出了一种新的 Shallow-Deep Attention-based Network (SDANet) 来对不同刺激下的 EEG 信号进行正确分类，它采用了 Attention-based Correlation Module (ACM) 来全局地发现听觉语音和 EEG 信号之间的关系，并且使用了 Shallow-Deep Similarity Classification Module (SDSCM) 基于浅层和深层学习得到的嵌入来决定分类结果。同时，还使用了多种训练策略和数据增强来提高模型鲁棒性，并在 Auditory EEG challenge 提供的数据集上进行了实验验证，结果表明该模型在匹配 - 不匹配轨迹方面比基线模型有显著的提升。