Using Ear-EEG to Decode Auditory Attention in Multiple-speaker Environment

作     者：Zhu, Haolin Yan, Yujie Xu, Xiran Ge, Zhongshu Tian, Pei Wu, Xihong Chen, Jing 

作者机构：Speech and Hearing Research Center School of Intelligence Science and Technology Peking University China Center for BioMed-X Research Academy for Advanced Interdisciplinary Studies Peking University China National Key Laboratory of General Artificial Intelligence Peking University China 

出 版 物：《arXiv》 (arXiv)

年 卷 期：2024年

核心收录：

主　　题：Electroencephalography 

摘      要：Auditory Attention Decoding (AAD) can help to determine the identity of the attended speaker during an auditory selective attention task, by analyzing and processing measurements of electroencephalography (EEG) data. Most studies on AAD are based on scalp-EEG signals in two-speaker scenarios, which are far from real application. Ear-EEG has recently gained significant attention due to its motion tolerance and invisibility during data acquisition, making it easy to incorporate with other devices for applications. In this work, participants selectively attended to one of the four spatially separated speakers’ speech in an anechoic room. The EEG data were concurrently collected from a scalp-EEG system and an ear-EEG system (cEEGrids). Temporal response functions (TRFs) and stimulus reconstruction (SR) were utilized using ear-EEG data. Results showed that the attended speech TRFs were stronger than each unattended speech and decoding accuracy was 41.3% in the 60s (chance level of 25%). To further investigate the impact of electrode placement and quantity, SR was utilized in both scalp-EEG and ear-EEG, revealing that while the number of electrodes had a minor effect, their positioning had a significant influence on the decoding accuracy. One kind of auditory spatial attention detection (ASAD) method, STAnet, was testified with this ear-EEG database, resulting in 93.1% in 1-second decoding window. The implementation code and database for our work are available on GitHub: https://***/zhl486/Ear_EEG_*** and Zenodo: https://***/records/10803261. Copyright © 2024, The Authors. All rights reserved.