2023/03/20 by Fan Cui, Cui, Fan, Liyong Guo +11
Computer Science · Neuroscience · #Audio and Speech Processing (eess.AS) #Blind Source Separation Techniques #Computation and Language (cs.CL) #EEG and Brain-Computer Interfaces #FOS: Biological sciences #FOS: Computer and information sciences #FOS: Electrical engineering #Neural dynamics and brain function #Neurons and Cognition (q-bio.NC) #Sound (cs.SD) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2303.10897
openalex publication_date 2023/03/20 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.