A Brain-Computer Interface (BCI) is designed for human-computer interactions without body movement. To improve the expression of input features closely related to a given BCI task, we propose a convolution network called GANet to analyze Event-Related Potential (ERP) in the BCI task. This model introduces the parallel convolution to extract multi-scale features in electroencephalogram (EEG) data. In addition, a Graph-based Attention (GA) mechanism is used to model interdependencies among different EEG channels. Experiments are conducted on a public dataset of 15 subjects in the specific-subject and cross-subject scenarios. The results indicate that the GANet achieves state-of-the-art performance with an accuracy of 99.75% in the specific-subject scenario and an accuracy of 81.37% in the cross-subject scenario. Different structures are discussed to analyze the contributions of the parallel convolution in feature extraction and the GA module in feature expression. GANet shows satisfied performance and good generalization in the BCI task. Our codes are publicly available at https://github.com/Debbie-85/GANet.

Emotion recognition is vital for mental and physical health, but current methods often rely on active user participation (e.g., questionnaires) or wearable sensors, limiting their real-world adoption. This paper proposes a non-contact BCG-based cross-subject emotion recognition framework. Subject-specific data distribution variations challenge model generalizability across domains. For this reason, we propose a LSCS-BDA method, extending Balanced Distribution Adaptation (BDA) with a Label Similarity Clustering for Selection (LSCS) module to optimize source domain selection. After feature extraction and baseline normalization, only the source domain individuals that are more similar to the target are included in the final source domain set through our method’s LSCS module (reducing training data to 9.78%), and finally the transfer is completed by the BDA algorithm. The experimental results show that the classification accuracy of our proposed method for three emotions of positive, negative, and neutral is 70.38%, which achieves higher accuracy and faster model training than classical transfer algorithms. The LSCS module in our method can be extended to other transfer learning methods. Our approach offers a new idea for contactless emotion recognition based on BCG and contributes to advancing its applications.

Multi-label arrhythmias classification is of great significance to the diagnosis of cardiovascular disease, and it is a challenging task as it requires identifying the label subset most related to each instance. In this paper, by integrating a deep residual neural network and auto-encoder, we propose an advanced deep neural network (DNN) framework with unified feature-aware and label embedding to perform multi-label arrhythmias classification involving 30 types of arrhythmias. Firstly, a deep residual neural network is built to extract the complex pathological features from varying-dimensional electrocardiograms (ECGs). Secondly, the mean square error loss is adopted to learn a latent space associating the deep pathological features and the corresponding label data, and then to achieve unified feature-label embedding. Thirdly, the label-correlation aware loss is introduced to optimize the auto-encoder architecture, which enables our model to exploit label-correlation for improved multi-label prediction. Our proposed DNN model can allow end-to-end training and prediction, which can perform feature-aware, label embedding, and label-correlation aware prediction in a unified framework. Finally, our proposed model is evaluated on the currently largest public dataset worldwide, and achieves the challenge metric scores of 0.492, 0.495, and 0.490 on the 12-lead, 3-lead, and all-lead version ECGs, respectively. The performance of our approach outperforms other current state-of-the-art methods in the leave-one-dataset-out cross-validation setting, which demonstrates that our approach has great competitiveness in identifying a wider range of multi-label arrhythmias.

Accurate and portable Blood Pressure (BP) monitoring is vital for managing cardiovascular diseases. However, existing wearable continuous BP monitoring technologies are often inaccurate and rely on external calibration, limiting their practical application in continuous BP monitoring. To address this challenge, we have developed a Wearable continuous non-invasive BP Monitor (WeBPM) equipped with a finger cuff sensor, capable of monitoring BP continuously and accurately within medical-grade precision. WeBPM integrates advanced finger oscillographic BP measurement technology to provide reliable self-calibration functionality. Moreover, Pulsatile Cycle Volume Adjustment Method (PCVAM) we proposed for the closed-loop phase can continuously track changes in vasomotor tone under a controlled frequency based on pulsatile cycles, thereby enabling continuous BP measurement. In comparative experiments with the Nexfin monitor, WeBPM demonstrates excellent performance in induced dynamic BP experiments, with measurement errors of (–1.4 $\pm$ 6.24) mmHg for Systolic BP (SBP) and (–0.82 $\pm$ 4.83) mmHg for Diastolic BP (DBP). Additionally, compared to clinical invasive reference measurements, WeBPM’s SBP and DBP measurement errors are (–1.74 $\pm$ 4.9) mmHg and (0.37 $\pm$ 3.28) mmHg, respectively, further proving its outstanding performance. These results highlight WeBPM’s potential in personalized health management and remote monitoring, offering a new solution for continuous non-invasive BP monitoring.