EEG-Based Sleep Quality Evaluation with Deep Transfer Learning

In this paper, we propose a subject-independent approach with deep transfer learning to evaluate the last-night sleep quality using EEG data. To reduce the intrinsic cross-subject differences of EEG data and background noise variations during signal acquisition, we adopt two classes of transfer learning methods to build subject-independent classifiers. One is to find a subspace by matrix decomposition and regularization theory, and the other is to learn the common shared structure with the deep autoencoder. The experimental results demonstrate that deep transfer learning model achieves the mean classification accuracy of 82.16% in comparison with the baseline SVM (65.74%) and outperforms other transfer learning methods. Our experimental results also indicate that the neural patterns of different sleep quality are discriminative and stable: the delta responses increase, the alpha responses decrease when sleep is partially deprived, and the neural patterns of 4-h sleep and 6-h sleep are more similar compared with 8-h sleep.