Mu Wenrui, Lu Bao-Liang
Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:5913-5916. doi: 10.1109/EMBC44109.2020.9176055.
With the quick development of dry electrode electroencephalography (EEG) acquisition technology, EEG-based sleep quality evaluation attracts more attention for its objective and quantitative merits. However, there hasn't been a standard experimental paradigm. This situation hinders the development of sleep quality evaluation method and technique. In this paper, we experimentally examine the performance of four typical experimental paradigms for EEG-based sleep quality evaluation and develop a new EEG dataset recorded by dry-electrode headset. To eliminate individual variation caused by subjects, we evaluate the four experimental paradigms using domain adaptation (DA) methods. Experimental results demonstrate that a relaxing paradigm is more effective than other attention concentration paradigms and achieves the average accuracy of 76.01%. Domain Adversarial Neural Network outperforms other DA methods and obtains 18.69% improvement on accuracy compared with transfer component analysis.
随着干电极脑电图（EEG）采集技术的快速发展，基于EEG的睡眠质量评估因其客观和定量的优点而备受关注。然而，目前尚未有标准的实验范式。这种情况阻碍了睡眠质量评估方法和技术的发展。在本文中，我们通过实验检验了四种基于EEG的睡眠质量评估典型实验范式的性能，并开发了一个由干电极头戴式设备记录的新EEG数据集。为了消除受试者引起的个体差异，我们使用域适应（DA）方法评估这四种实验范式。实验结果表明，一种放松范式比其他注意力集中范式更有效，平均准确率达到76.01%。域对抗神经网络优于其他DA方法，与转移成分分析相比，准确率提高了18.69%。
