用耳部脑电检测多人说话环境中的听觉注意力，准确率达93.1%。
Using Ear-EEG to Decode Auditory Attention in Multiple-speaker Environment
- 采用耳部脑电（ear-EEG）与刺激重建技术分析听觉注意力
- 在1秒窗口内实现93.1%的注意力识别准确率，显著高于随机水平
- 验证了电极位置对解码性能影响大，适合可穿戴设备应用
听觉注意力解码（AAD）可通过分析脑电图（EEG）数据判断在多说话人任务中关注的目标说话人。现有研究多基于头皮脑电（scalp-EEG）在双说话人场景，难以应用于真实环境。耳部脑电（ear-EEG）因运动耐受性强、隐蔽性好，更易集成于其他设备。本研究在消音室中让参与者从四个空间分离的说话人中选择性注意一个。同时采集头皮-EEG与耳部-EEG（cEEGrids）数据。利用时间响应函数（TRFs）和刺激重构（SR）分析耳部脑电信号。结果显示，关注语音的TRFs强于未关注语音，60秒内的解码准确率为41.3%（随机水平为25%）。进一步通过SR对比头皮与耳部脑电，发现电极数量影响较小，但位置显著影响解码精度。基于该耳部脑电数据集，验证了一种听觉空间注意力检测方法（STAnet），在1秒窗口内达到93.1%的准确率。相关代码与数据已公开于GitHub与Zenodo。
原文摘要 · Abstract (English)
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://github.com/zhl486/Ear_EEG_code.git and Zenodo: https://zenodo.org/records/10803261.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。