Sleep stage classification based on physiological time-series is essential for sleep quality evaluation and the diagnosis of sleep disorders in clinical practice. Existing machine learning studies have achieved adequate results in sleep stage classification. However, those methods neglect the significance of simultaneously capturing the interactivity and heterogeneity of physiological signals. In this paper, we propose a novel Sleep Heterogeneous Graph Neural Network (SleepHGNN) to employ these essential features. The SleepHGNN is a deep graph network consisting of Heterogeneous Graph Transformer layers, which are composed of a Heterogeneous Message Passing module for capturing the heterogeneity and a Target-Specific Aggregation module for capturing the interactivity of physiological signals. The experiments show that the SleepHGNN outperforms the state-of-the-art models on the sleep stage classification task. The source code of SleepHGNN is available at: https://github.com/zhouyh310/SleepHGNN.

Sleep staging is the basis of sleep medicine for diagnosing psychiatric and neurodegenerative diseases. However, the existing sleep staging methods ignore the fact that multi-modal physiological signals are heterogeneous, and different modalities contribute to sleep staging with distinct impacts on specific stages. Therefore, how to model the heterogeneity of multi-modal signals and adaptively utilize the multi-modal signals for sleep staging remains challenging. To address the above challenges, we design a Two-Stream Squeeze-and-Excitation Network (TSSEN) to capture the features of electroencephalogram (EEG) and electrooculogram (EOG) for sleep staging. The TS-SEN is made up of two independent feature extraction networks for modeling the heterogeneity and a Multi-modal Squeeze-and-Excitation feature fusion module for adaptively utilizing the multi-modal signals. Experiments demonstrate that the TSSEN is superior to the baseline models on the public sleep staging dataset. The implementation code of TS-SEN available at https://github.com/xiyangcai/TS-SEN

Sleep is one of the most fundamental physiological activities of human beings. Automatic sleep staging can efficiently assist human experts to diagnose the sleep health of people. However, most of the existing methods only considered one or two kinds of time-domain, frequency-domain, and spatial-domain information from EEG signals. Therefore, how to make full use of the complementarity of different features of EEG signals is still a challenge. To tackle this challenge, in this paper we design BrainSleepNet to capture the comprehensive features of multivariant EEG signals for automatic sleep staging. BrainSleepNet consists of an EEG temporal feature extraction module and an EEG spectral-spatial feature extraction module for the temporal-spectral-spatial representation of EEG signals. To the best of our knowledge, it is the first attempt to integrate EEG temporal-spectral-spatial features simultaneously in a unified model for sleep staging. Experiments on the benchmark dataset MASSSS3 demonstrate that BrainSleepNet outperforms all baseline models. The implementation code of BrainSleepNet is available at https://github.com/ziyujia/sleep.

Multimedia stimulation of brain activities has not only become an emerging field for intensive research, but also achieves important progress in the electroencephalogram (EEG) emotion classification based on brain activities. However, how to make full use of different EEG features and the discriminative local patterns among the features for different emotions is challenging. Existing models ignore the complementarity among the spatial-spectral-temporal features and discriminative local patterns in all features, which limits the classification ability of the models to a certain extent. In this paper, we propose a novel spatial-spectral-temporal based attention 3D dense network, named SST-EmotionNet, for EEG emotion recognition. The main advantage of the SST-EmotionNet is the simultaneous integration of spatial-spectral-temporal features in a unified network framework. Meanwhile, a 3D attention mechanism is designed to adaptively explore discriminative local patterns. Extensive experiments on two real-world datasets demonstrate that the SST-EmotionNet outperforms the state-of-the-art baselines.