Sleep is an essential part of human life, and the quality of one’s sleep is also an important indicator of one’s health. Analyzing the Electroencephalogram (EEG) signals of a person during sleep makes it possible to understand the sleep status and give relevant rest or medical advice. In this paper, a decent amount of artificial data generated with a data augmentation method based on Discrete Cosine Transform from a small amount of real experimental data of a specific individual is introduced. A classification model with an accuracy of 92.85% has been obtained. By mixing the data augmentation with the public database and training with the EEGNet, we obtained a classification model with significantly higher accuracy for the specific individual. The experiments have demonstrated that we can circumvent the subject-independent problem in sleep EEG in this way and use only a small amount of labeled data to customize a dedicated classification model with high accuracy.

睡眠是人类生活中必不可少的一部分，睡眠质量也是一个人健康状况的重要指标。通过分析人在睡眠期间的脑电图（EEG）信号，可以了解睡眠状态并给出相关的休息或医疗建议。本文介绍了通过基于离散余弦变换的数据增强方法从特定个体的少量真实实验数据生成的大量人工数据。得到了准确率为92.85%的分类模型。通过将数据增强与公共数据库混合并使用 EEGNet 进行训练，我们获得了针对特定个体的准确度明显更高的分类模型。实验证明，我们可以通过这种方式规避睡眠脑电图的受试者无关问题，仅使用少量标记数据来定制高精度的专用分类模型。