Examining Four Experimental Paradigms for EEG-Based Sleep Quality Evaluation with Domain Adaptation.

Abstract

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.