论文标题
将歌曲与脑电图分类
Classifying Songs with EEG
论文作者
论文摘要
这项研究旨在使用机器学习方法来表征脑电图对音乐的反应。具体而言，我们研究了脑电图响应中的共鸣与单个美学享受的相关性。受音乐处理作为共鸣的概念的启发，我们假设美学体验的强度是基于参与者脑电图吸引感知输入的程度。为了检验此和其他假设，我们从20位主题中构建了一个脑电图数据集，以随机顺序收听12个两分钟的歌曲。在进行预处理和功能构建之后，我们使用此数据集训练和测试多个机器学习模型。
This research study aims to use machine learning methods to characterize the EEG response to music. Specifically, we investigate how resonance in the EEG response correlates with individual aesthetic enjoyment. Inspired by the notion of musical processing as resonance, we hypothesize that the intensity of an aesthetic experience is based on the degree to which a participants EEG entrains to the perceptual input. To test this and other hypotheses, we have built an EEG dataset from 20 subjects listening to 12 two minute-long songs in random order. After preprocessing and feature construction, we used this dataset to train and test multiple machine learning models.
