Continuous Music-Emotion Recognition Based on Electroencephalogram
Continuous Music-Emotion Recognition Based on Electroencephalogram
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DOI：
10.1587/transinf.2015edp7251
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发表时间：
2016-04
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影响因子：
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通讯作者：
Nattapong Thammasan;K. Moriyama;Ken-ichi Fukui;M. Numao
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文献类型：
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作者：
Nattapong Thammasan;K. Moriyama;Ken-ichi Fukui;M. Numao
在过去的十年里，利用脑电(EEG)对听音乐的受试者进行情绪识别的研究越来越活跃。然而，以前的作品并没有考虑单个音乐作品中的情感波动。在本研究中，我们提出了一种基于脑波信号的连续音乐情感识别方法。在考虑情绪的被试依存性和时变性的基础上，本实验在唤醒效价空间中加入了自我报告和持续情绪标注。采用分维(FD)和功率谱密度(PSD)方法从原始脑电信号中提取信息特征，然后应用情绪分类算法对二值情绪进行区分。根据我们的实验结果，FD在唤醒和效度分类上都略优于PSD方法，而且FD与情绪报告的相关性高于PSD。此外，基于脑电的音乐听过程中的连续情绪识别被发现是一种跟踪情绪报告振荡的有效方法，并为更好地理解人类的情绪过程提供了机会。关键词：音乐、情感、脑电
Research on emotion recognition using electroencephalogram (EEG) of subjects listening to music has become more active in the past decade. However, previous works did not consider emotional oscillations within a single musical piece. In this research, we propose a continuous music-emotion recognition approach based on brainwave signals. While considering the subject-dependent and changing-over-time characteristics of emotion, our experiment included self-reporting and continuous emotion annotation in the arousal-valence space. Fractal dimension (FD) and power spectral density (PSD) approaches were adopted to extract informative features from raw EEG signals and then we applied emotion classification algorithms to discriminate binary classes of emotion. According to our experimental results, FD slightly outperformed PSD approach both in arousal and valence classification, and FD was found to have the higher correlation with emotion reports than PSD. In addition, continuous emotion recognition during music listening based on EEG was found to be an effective method for tracking emotional reporting oscillations and provides an opportunity to better understand human emotional processes. key words: music, emotion, electroencephalogram