Recent studies have demonstrated that the representations of artificial neural networks (ANNs) can exhibit notable similarities to cortical representations when subjected to identical auditory sensory inputs. In these studies, the ability to predict cortical representations is probed by regressing from ANN representations to cortical representations. Building upon this concept, our approach reverses the direction of prediction: we utilize ANN representations as a supervisory signal to train recognition models using noisy brain recordings obtained through non-invasive measurements. Specifically, we focus on constructing a recognition model for music identification, where electroencephalography (EEG) brain recordings collected during music listening serve as input. By training an EEG recognition model to predict ANN representations-representations associated with music identification-we observed a substantial improvement in classification accuracy. This study introduces a novel approach to developing recognition models for brain recordings in response to external auditory stimuli. It holds promise for advancing brain-computer interfaces (BCI), neural decoding techniques, and our understanding of music cognition. Furthermore, it provides new insights into the relationship between auditory brain activity and ANN representations.

翻译：近期研究表明，在接收相同听觉感官输入时，人工神经网络（ANN）的表征与大脑皮层表征存在显著相似性。现有研究通常通过从ANN表征回归到皮层表征来探索其预测能力。基于这一理念，我们的方法逆转了预测方向：利用ANN表征作为监督信号，通过非侵入式测量获得的噪声脑电信号来训练识别模型。具体而言，我们专注于构建音乐识别模型，其中以音乐聆听期间采集的脑电图（EEG）信号作为输入。通过训练EEG识别模型来预测与音乐识别相关的ANN表征，我们观察到分类准确率得到显著提升。本研究提出了一种构建响应外部听觉刺激的脑电信号识别模型的新方法，对推进脑机接口（BCI）、神经解码技术及音乐认知理解具有重要价值。此外，该研究为理解听觉脑活动与ANN表征之间的关系提供了新的视角。