AADNet：基于线索掩蔽范式探索EEG时空信息以快速准确检测听觉注意的方位与音色
AADNet: Exploring EEG Spatiotemporal Information for Fast and Accurate Orientation and Timbre Detection of Auditory Attention Based on A Cue-Masked Paradigm
AI总结：
提出线索掩蔽范式与端到端AADNet，利用0.5秒EEG时空信息快速解码听觉注意的方位和音色，准确率分别达93.46%和91.09%。
摘要：
从脑电图（EEG）中解码听觉注意，可以推断用户在嘈杂环境中正在关注哪个声源。解码算法和实验范式设计对于该技术在实际应用中的发展至关重要。为模拟真实场景，本研究提出了一种线索掩蔽听觉注意范式，以避免实验前的信息泄漏。为了在低延迟下获得较高的解码准确率，本文提出了一种端到端深度学习模型AADNet，用于利用EEG信号短时间窗中的时空信息。结果表明，在0.5秒EEG窗口下，AADNet在解码听觉方位注意（OA）和音色注意（TA）时分别取得了93.46%和91.09%的平均准确率。其显著优于五种既往方法，并且不需要原始声源的知识。本工作证明了从EEG信号中快速、准确地检测听觉注意的方位和音色是可能的。这些结果对于实时多属性听觉注意解码具有良好前景，并有助于推动神经导向助听器及其他辅助聆听设备的应用。
英文摘要：
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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