Electroencephalogram data are easily affected by artifacts, and a drift may occur during the signal acquisition process. At present, most research focuses on the automatic detection and elimination of artifacts in electrooculograms, electromyograms and electrocardiograms. However, electroencephalogram drift data, which affect the real-time performance, are mainly manually calibrated and abandoned. An emotion classification method based on 1/f fluctuation theory is proposed to classify electroencephalogram data without removing artifacts and drift data. The results show that the proposed method can still achieve a great classification accuracy of 75% in cases in which artifacts and drift data exist when using the support vector machine classifier. In addition, the real-time performance of the proposed method is guaranteed.

中文翻译：

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基于1/f涨落理论的脑电情绪分类方法

脑电图数据容易受伪影影响，在信号采集过程中可能会出现漂移。目前，大多数研究集中在眼电图、肌电图和心电图伪影的自动检测和消除。然而，影响实时性的脑电图漂移数据主要是手动校准和废弃的。提出一种基于1/f涨落理论的情绪分类方法，在不去除伪影和漂移数据的情况下对脑电数据进行分类。结果表明，在使用支持向量机分类器存在伪影和漂移数据的情况下，所提出的方法仍然可以达到75%的分类准确率。此外，保证了所提出方法的实时性能。