During music listening, cortical activity encodes both acoustic and expectation-related information. Prior work has shown that ANN representations resemble cortical representations and can serve as supervisory signals for EEG recognition. Here we show that distinguishing acoustic and expectation-related ANN representations as teacher targets improves EEG-based music identification. Models pretrained to predict either representation outperform non-pretrained baselines, and combining them yields complementary gains that exceed strong seed ensembles formed by varying random initializations. These findings show that teacher representation type shapes downstream performance and that representation learning can be guided by neural encoding. This work points toward advances in predictive music cognition and neural decoding. Our expectation representation, computed directly from raw signals without manual labels, reflects predictive structure beyond onset or pitch, enabling investigation of multilayer predictive encoding across diverse stimuli. Its scalability to large, diverse datasets further suggests potential for developing general-purpose EEG models grounded in cortical encoding principles.

翻译：在音乐聆听过程中，皮层活动同时编码声学信息与期望相关信息。先前研究表明，人工神经网络（ANN）表征与皮层表征相似，可作为脑电图（EEG）识别的监督信号。本文证明，将声学相关与期望相关的ANN表征区分为教师目标，能够提升基于EEG的音乐识别性能。经预训练以预测任一类表征的模型均优于未预训练的基线模型，而结合两类表征可产生互补性增益，其效果超越通过不同随机初始化形成的强种子集成模型。这些发现表明，教师表征类型影响下游任务性能，且表征学习可通过神经编码进行引导。本研究为预测性音乐认知与神经解码领域的进展提供了方向。我们提出的期望表征直接由原始信号计算获得，无需人工标注，能够反映超越起始点或音高的预测性结构，从而支持跨多样刺激的多层预测编码研究。该表征可扩展至大规模多样化数据集，进一步表明基于皮层编码原理开发通用EEG模型具有潜在可行性。