用脑电波快速精准识别听觉注意力方向与音色，无需原始音频。
AADNet: Exploring EEG Spatiotemporal Information for Fast and Accurate Orientation and Timbre Detection of Auditory Attention Based on A Cue-Masked Paradigm
- 基于0.5秒脑电信号，端到端模型捕捉时空特征。
- 方向与音色识别准确率分别达93.46%和91.09%，优于五种旧方法。
- 适用于实时助听设备，避免信息泄露，适合真实场景应用。
从脑电图（EEG）中解码听觉注意可推断用户在嘈杂环境中关注的声源。解码算法与实验范式设计对技术实用化至关重要。为模拟真实场景，本研究提出一种线索掩蔽听觉注意范式，避免实验前信息泄露。为实现高精度低延迟解码，提出端到端深度学习模型AADNet，利用短时窗脑电信号的时空信息。结果表明，在0.5秒脑电窗口下，AADNet在听觉方向注意（OA）和音色注意（TA）解码中的平均准确率分别达到93.46%和91.09%，显著优于五种已有方法，且无需原始音频知识。该工作证明了从脑电信号中快速准确检测听觉注意的方向与音色是可行的，对实时多属性听觉注意解码具有前景，有助于神经引导助听器等辅助听觉设备的应用。
原文摘要 · Abstract (English)
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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