EEG Signal Power Prediction Using DEAP Dataset
EEG Signal Power Prediction Using DEAP Dataset
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DOI：
10.1109/iciibms55689.2022.9971594
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发表时间：
2022-11
期刊：
影响因子：
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通讯作者：
Ángel Muñoz-González;Ryota Horie
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文献类型：
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作者：
Ángel Muñoz-González;Ryota Horie
当我们听音乐时，我们的情绪会因为大脑反应的变化而改变。正因为如此，出现了大量通过机器学习技术在听音乐时使用大脑信号对情绪反应进行分类的研究项目。相反，据我们所知，没有以前的研究试图通过机器学习技术来估计音乐刺激下脑电图（EEG）反应的动态变化。因此，在本文中，我们提出了一种方法来预测和预测音乐刺激下的EEG信号的变化。使用DEAP数据集，我们将对音乐刺激的EEG响应分成一秒长的帧。之后，我们通过两个单尾Wilcoxon秩和检验比较了连续帧的脑信号功率的变化。该测试允许我们将第二帧中的变化标记为与第一帧相比"更低"、"类似"或"更高"的信号。然后，我们尝试使用支持向量机（SVM）分类器预测这些变化，该分类器具有分层5倍验证，具有不同的输入组合（仅音乐，仅大脑信号或两者的组合）。由于使用了不平衡数据的多标签分类，我们通过F1分数来衡量结果。当使用不同通道和频带的前一个第二脑信号时，特别是在额叶F3和F4通道中，获得了信号功率变化的偶然水平预测。
When we listen to music, our emotions can change due to changes in our brain response. Because of this, a large number of research projects for classifying the emotional response using brain signals when listening to music through machine learning techniques emerged. On the contrary, to our knowledge, there is no previous research attempting to estimate the dynamic changes in the electroencephalogram (EEG) response under music stimuli through machine learning techniques. Therefore, in this manuscript, we proposed an approach to predict and anticipate changes in the EEG signal under music stimuli. Using the DEAP dataset, we split the EEG response to music stimuli into one-second length frames. After that, we compared the changes in the power of the brain signal of consecutive frames through two one-tailed Wilcoxon rank-sum tests. This test allowed us to label the changes in the second frame as "lower", "similar" or "higher" signal compared to the first frame. Then, we attempted to predict these changes using a Support-Vector Machine (SVM) classifier with stratified 5-fold validation with different input combinations (only music, only brain signal, or a combination of both). Due to the use of multi-label classification with imbalanced data, we measured the results through F1-Scores. Over chance level predictions of the changes of signal power were obtained when using the previous second brain signal for the different channels and bands, especially in the frontal F3 and F4 channels.