Although emotion detection using electroencephalogram (EEG) data has become a highly active area of research over the last decades, little attention has been paid to stimulus familiarity, a crucial subjectivity issue. Using both our experimental data and a sophisticated database (DEAP dataset), we investigated the effects of familiarity on brain activity based on EEG signals. Focusing on familiarity studies, we allowed subjects to select the same number of familiar and unfamiliar songs; both resulting datasets demonstrated the importance of reporting self-emotion based on the assumption that the emotional state when experiencing music is subjective. We found evidence that music familiarity influences both the power spectra of brainwaves and the brain functional connectivity to a certain level. We conducted an additional experiment using music familiarity in an attempt to recognize emotional states; our empirical results suggested that the use of only songs with low familiarity levels can enhance the performance of EEG-based emotion classification systems that adopt fractal dimension or power spectral density features and support vector machine, multilayer perceptron or C4.5 classifier. This suggests that unfamiliar songs are most appropriate for the construction of an emotion recognition system.

中文翻译：

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基于EEG的情绪识别中的熟悉效应。

尽管在过去的几十年中，使用脑电图（EEG）数据进行情感检测已成为研究的一个非常活跃的领域，但很少有人关注刺激性的熟悉性，这是一个至关重要的主观性问题。使用我们的实验数据和复杂的数据库（DEAP数据集），我们基于EEG信号研究了熟悉程度对大脑活动的影响。着重于熟悉性研究，我们允许对象选择相同数量的熟悉和不熟悉的歌曲；这两个结果数据集都基于以下假设：报告自我情感的重要性，前提是人们在体验音乐时的情感状态是主观的。我们发现有证据表明，音乐的熟悉程度会在一定程度上影响脑电波的功率谱和大脑功能的连通性。我们使用音乐熟悉度进行了另一个实验，目的是识别情绪状态。我们的经验结果表明，仅使用熟悉程度较低的歌曲可以提高基于EEG的情绪分类系统的性能，该系统采用分形维数或功率谱密度特征并支持向量机，多层感知器或C4.5分类器。这表明，陌生的歌曲最适合构建情绪识别系统。