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

• URL: http://arxiv.org/abs/2501.03571v1
• Date: Tue, 07 Jan 2025 06:51:17 GMT
• Title: AADNet: Exploring EEG Spatiotemporal Information for Fast and Accurate Orientation and Timbre Detection of Auditory Attention Based on A Cue-Masked Paradigm
• Authors: Keren Shi, Xu Liu, Xue Yuan, Haijie Shang, Ruiting Dai, Hanbin Wang, Yunfa Fu, Ning Jiang, Jiayuan He,
• Abstract summary: Auditory attention decoding from electroencephalogram (EEG) could infer to which source the user is attending in noisy environments. This study proposed a cue-masked auditory attention paradigm to avoid information leakage before the experiment. An end-to-end deep learning model, AADNet, was proposed to exploit thetemporal information from the short time window EEG signals.
• Score: 4.479495549911642
• License:
• 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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