English(EN) EEG-Based Prediction of Music Enjoyment Using Deep Learning with Explainable Temporal Analysis

深度学习模型利用脑电图数据预测音乐享受

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-16 04:00

研究人员开发了利用脑电图（EEG）数据预测音乐享受的深度学习模型。比较了六种模型，其中DeepConvNet、CNN-LSTM和GNN-CNN的平均绝对误差最低。时间卷积网络（TCN）与听众评分的相关性最高。可解释性分析用于识别影响TCN预测的关键时间段，为基于EEG的享受回归提供了基准。 AI

影响这项研究可能导致对音乐享受等主观体验进行更客观的衡量，可能影响个性化媒体推荐和治疗应用等领域。

排序理由该条目是一篇学术论文，详细介绍了使用深度学习和脑电图数据预测音乐享受的新方法和基准。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.LG TIER_1 English(EN) · Parshav Singla, Simranjit Kaur, Maanya Walia, Niharika · 2026-10-16 04:00

基于深度学习和可解释时间分析的脑电图音乐愉悦度预测

arXiv:2610.20795v1 Announce Type: new Abstract: Understanding why listeners enjoy music is difficult because the experience is subjective and changes as a piece unfolds. Electroencephalography (EEG) offers a non-invasive way to study neural responses during listening, yet earlier…