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This work addresses the adoption of Machine Learning classifiers and Convolutional Neural Networks to improve the performance of highly wearable, single-channel instrumentation for Brain-Computer Interfaces. The proposed measurement system is based on the classification of Steady- State Visually Evoked Potentials (SSVEPs). In particular, Head-Mounted Displays for Augmented Reality are used to generate and display the flickering stimuli for the SSVEPs elicitation. Four experiments were conducted by employing, in turn, a different Head-Mounted Display. For each experiment, two different algorithms were applied and compared with the state-of-the-art-techniques. Furthermore, the impact of different Augmented Reality technologies in the elicitation and classification of SSVEPs was also explored. The experimental metrological characterization demonstrates (i) that the proposed Machine Learning-based processing strategies providea significantenhancement of theSSVEPclassification accuracy with respect to the state of the art, and (ii) that choosing an adequate Head-Mounted Display is crucial to obtain acceptable performance. Finally, it is also shown that the adoption of inter-subjective validation strategies such as the Leave-One-Subject-Out Cross Validation successfully leads to an increase in the inter-individual 1- $\sigma$ reproducibility: this, in turn, anticipates an easier development of ready-to-use systems. [ABSTRACT FROM AUTHOR]

In this work, an augmented-reality (AR) system for monitoring patient’s vitals in real time during surgical procedures is proposed and metrologically characterized in terms of transmission error rates and latency. These specifications, in fact, are crucial to ensure real-time response which is a critical requirement for Health applications. The proposed system automatically acquires the vitals from the operating room instrumentation, and shows them in real time directly on a set of wearable AR glasses. In this way, the surgical team has a real-time visualization of a comprehensive set of patient’s information, without constantly looking at the instrumentation. The system was designed to ensure modularity, flexibility, ease of use and, most importantly, a reliable communication. The system was designed, implemented and validated experimentally through on-field tests carried out at the Academic Hospital of Federico II University, using instrumentation typically available in the operating room: namely, a respiratory ventilator and a patient monitor. Results of the experimental validation highlight the need for the metrological approach in conceiving advanced monitoring surgical procedures in operating room. [ABSTRACT FROM AUTHOR]

An instrument for remote control of the robot by wearable brain–computer interface (BCI) is proposed for rehabilitating children with attention-deficit/hyperactivity disorder (ADHD). Augmented reality (AR) glasses generate flickering stimuli, and a single-channel electroencephalographic BCI detects the elicited steady-state visual evoked potentials (SSVEPs). This allows benefiting from the SSVEP robustness by leaving available the view of robot movements. Together with the lack of training, a single channel maximizes the device’s wearability, fundamental for the acceptance by ADHD children. Effectively controlling the movements of a robot through a new channel enhances rehabilitation engagement and effectiveness. A case study at an accredited rehabilitation center on ten healthy adult subjects highlighted an average accuracy higher than 83%, with information transfer rate (ITR) up to 39 b/min. Preliminary further tests on four ADHD patients between six- and eight-years old provided highly positive feedback on device acceptance and attentional performance. [ABSTRACT FROM AUTHOR]