Prognostic assessment of patients with disorders of consciousness (DoC) remains one of the most challenging problems in contemporary medicine. The long treatment cycle and high costs of treatment are heavy burdens to our society. In this paper, we use deep network to investigate potential indicators of consciousness within brain signals of DoC patients. In the experiments, we study P300 and resting-state Electroencephalogram (rs-EEG) signals of 22 DoC patients to investigate neural correlation between brain signals and the improvement of consciousness. Synergistic integration of P300 and rs-EEG signals demonstrated superior predictive proficiency for cross-subject and cross-paradigm prognosis in DoC, achieving an accuracy of 81.1%. Our investigation is the first known to the literature to combine P300 and rs-EEG signals for analyzing DoC. This novel approach leverages advanced neural network models to elucidate the complex neural patterns associated with DoC, setting a precedent for future research in the field.

Sleep staging holds significant importance in clinical medicine, aiding in the diagnosis of various disorders related to sleep and cognition. However, manually annotating a large amount of sleep data is time-consuming and labor-intensive, making it difficult to achieve. Efficiently utilizing these unannotated data poses a challenging task. We propose a novel self-supervised learning method with Temporal-split Contrastive and Electrode Autoencoder (TsC-EA) for sleep staging. We demonstrate that our method achieves state-of-the-art performance in self-supervised learning on SleepEDF and MASS-SS3. Moreover, experimental results indicate that our method can surpass the performance of supervised learning methods using only 10% of labeled data. Additionally, we explore the application of self-supervised learning in patients with disorder of consciousness. It can assist in diagnosing the severity of DoC through analysis of sleep staging. Staging the sleep patterns of patients with disorders of consciousness can help in diagnosing the severity of their condition.