Joint Semi-Supervised Feature Auto-Weighting and Classification Model for EEG-Based Cross-Subject Sleep Quality Evaluation

Abstract

Measuring the sleep quality is important or even crucial for people who are engaged in dangerous jobs such as the high-speed train drivers. Since the scalp EEG data are generated by the neural activities of the brain cortex, it is collected from subjects with different hours of sleep time (4 hours, 6 hours and 8 hours) to conduct sleep quality evaluation. To suppress the cross-subject variances of EEG data, in this paper, we propose a joint feature auto-weighting and semi-supervised classification model, termed GRLSR, which is formulated by introducing an auto-weighting variable into the least square regression to adaptively and quantitatively measure the importance of each dimension of the feature. Once the model is solved, besides the measurement results, we can use the auto-weighting variable to 1) analyze the importance of each frequency band in sleep quality expression and 2) identify the capacity of different channels connecting to the sleep effect. Therefore, the proposed GRLSR is a pure data-driven computing model for EEG-based cross-subject sleep quality evaluation. Experimental results show its effectiveness.