An EEG-based Brain-Computer Interface (BCI) is a system that enables a user to communicate with and intuitively control external devices solely using the user’s intentions. Current EEG-based BCI research usually involves a subject-specific adaptation step before a BCI system is ready to be employed by a new user. However, the subject-independent scenario, in which a well-trained model can be directly applied to new users without pre-calibration, is particularly desirable yet rarely explored. Considering this critical gap, our focus in this paper is the subject-independent scenario of EEG-based human intention recognition. We present a Graph-based Hierarchical Attention Model (G-HAM) that utilizes the graph structure to represent the spatial information of EEG sensors and the hierarchical attention mechanism to focus on both the most discriminative temporal periods and EEG nodes. Extensive experiments on a large EEG dataset containing 105 subjects indicate that our model is capable of exploiting the underlying invariant EEG patterns across different subjects and generalizing the patterns to new subjects with better performance than a series of state-of-the-art and baseline approaches.

While the Internet of Things (IoT) is growing at a staggering pace, the demand for context-driven intelligence will be a fundamental factor for IoT sustainability, growth, interoperability and acceptance. In this paper, we propose an operational platform called Context-as-a-Service (CoaaS) which enables smart things and IoT silos to discover, validate and share relevant and dependable context. The CoaaS platform consists of context query engine, context reasoning engine, and context storage management system for scalable and performance-oriented context management. We demonstrate features and functionality of CoaaS with a smart city use case “Smart parking and charging for electrical vehicles” partnering with a large vehicle manufacturing company.

Background: A nanomaterial-based electronic-skin (E-Skin) wearable sensor has been successfully used for detecting and measuring body movements such as finger movement and foot pressure. The ultrathin and highly sensitive characteristics of E-Skin sensor make it a suitable alternative for continuously out-of-hospital lumbar–pelvic movement (LPM) monitoring. Monitoring these movements can help medical experts better understand individuals’ low back pain experience. However, there is a lack of prior studies in this research area. Therefore, this paper explores the potential of E-Skin sensors to detect and measure the anatomical angles of lumbar–pelvic movements by building a linear relationship model to compare its performance to clinically validated inertial measurement unit (IMU)-based sensing system (ViMove). Methods: The paper first presents a review and classification of existing wireless sensing technologies for monitoring of body movements, and then it describes a series of experiments performed with E-Skin sensors for detecting five standard LPMs including flexion, extension, pelvic tilt, lateral flexion, and rotation, and measure their anatomical angles. The outputs of both E-Skin and ViMove sensors were recorded during each experiment and further analysed to build the comparative models to evaluate the performance of detecting and measuring LPMs. Results: E-Skin sensor outputs showed a persistently repeating pattern for each movement. Due to the ability to sense minor skin deformation by E-skin sensor, its reaction time in detecting lumbar–pelvic movement is quicker than ViMove by ~1 s. Conclusions: E-Skin sensors offer new capabilities for detecting and measuring lumbar–pelvic movements. They have lower cost compared to commercially available IMU-based systems and their non-invasive highly stretchable characteristic makes them more comfortable for long-term use. These features make them a suitable sensing technology for developing continuous, out-of-hospital real-time monitoring and management systems for individuals with low back pain.