AADNet: Exploring EEG Spatiotemporal Information for Fast and Accurate Orientation and Timbre Detection of Auditory Attention Based on A Cue-Masked Paradigm

Keren Shi, Xu Liu,Xue Yuan, Haijie Shang, Ruiting Dai, Hanbin Wang,Yunfa Fu,Ning Jiang,Jiayuan He

IEEE transactions on neural systems and rehabilitation engineering a publication of the IEEE Engineering in Medicine and Biology Society（2025）

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Abstract

Auditory attention decoding from electroencephalogram (EEG) could infer to which source the user is attending in noisy environments. Decoding algorithms and experimental paradigm designs are crucial for the development of technology in practical applications. To simulate real-world scenarios, this study proposed a cue-masked auditory attention paradigm to avoid information leakage before the experiment. To obtain high decoding accuracy with low latency, an end-to-end deep learning model, AADNet, was proposed to exploit the spatiotemporal information from the short time window of EEG signals. The results showed that with a 0.5-second EEG window, AADNet achieved an average accuracy of 93.46% and 91.09% in decoding auditory orientation attention (OA) and timbre attention (TA), respectively. It significantly outperformed five previous methods and did not need the knowledge of the original audio source. This work demonstrated that it was possible to detect the orientation and timbre of auditory attention from EEG signals fast and accurately. The results are promising for the real-time multi-property auditory attention decoding, facilitating the application of the neuro-steered hearing aids and other assistive listening devices.

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Key words

Auditory attention decoding,brain-computer interface (BCI),neuro-steered hearing device,electroencephalography (EEG)

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【要点】：论文提出了一种基于EEG信号的端到端深度学习模型AADNet，能在0.5秒的短时间窗口内快速准确地进行听觉注意的方向和音色检测。

【方法】：通过设计一种新的实验范式——提示遮蔽听觉注意范式，避免了实验前的信息泄露，并利用AADNet模型提取EEG信号的时空信息。

【实验】：使用0.5秒的EEG窗口，AADNet在方向注意和音色注意上分别达到了平均93.46%的准确率，显著优于之前的五种方法，且不需要原始音频源的知识。

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