The spatial auditory attention decoding (Sp-AAD) technology aims to determine the direction of auditory attention in multi-talker scenarios via neural recordings. Despite the success of recent Sp-AAD algorithms, their performance is hindered by trial-specific features in EEG data. This study aims to improve decoding performance against these features. Studies in neuroscience indicate that spatial auditory attention can be reflected in the topological distribution of EEG energy across different frequency bands. This insight motivates us to propose Prototype Training, a neuroscience-inspired method for Sp-AAD. This method constructs prototypes with enhanced energy distribution representations and reduced trial-specific characteristics, enabling the model to better capture auditory attention features. To implement prototype training, an EEGWaveNet that employs the wavelet transform of EEG is further proposed. Detailed experiments indicate that the EEGWaveNet with prototype training outperforms other competitive models on various datasets, and the effectiveness of the proposed method is also validated. As a training method independent of model architecture, prototype training offers new insights into the field of Sp-AAD.

翻译：空间听觉注意解码（Sp-AAD）技术旨在通过神经记录确定多说话者场景中听觉注意的方向。尽管近期的Sp-AAD算法取得了一定成功，但其性能受到脑电图数据中试次特异性特征的制约。本研究旨在提升针对这些特征的解码性能。神经科学研究表明，空间听觉注意可以反映在不同频段上脑电图能量的拓扑分布。这一见解启发我们提出原型训练——一种受神经科学启发的Sp-AAD方法。该方法构建具有增强的能量分布表示和降低的试次特异性特征的原型，使模型能更好地捕捉听觉注意特征。为实现原型训练，本文进一步提出了采用脑电图小波变换的EEGWaveNet。详细实验表明，结合原型训练的EEGWaveNet在多个数据集上优于其他竞争模型，所提方法的有效性也得到了验证。作为一种独立于模型架构的训练方法，原型训练为Sp-AAD领域提供了新的思路。