Brain computer interfaces: an engineering view. Design, implementation and test of a SSVEP-based BCI.

This thesis presents the realization of a compact, yet flexible BCI platform, which, when compared to most commercially-available solution, can offer an optimal trade-off between the following requirements: (i) minimal, easy experimental setup; (ii) flexibility, allowing simultaneous studies on other bio-potentials; (iii) cost effectiveness (e.g. < 1000 €); (iv) robust design, suitable for operation outside lab environments. The thesis encompasses all the project phases, from hardware design and realization, up to software and signal processing. The work started from the development of the hardware acquisition unit. It resulted in a compact, battery-operated module, whose medium-to-large scale production costs are in the range of 300 €. The module features 16 input channels and can be used to acquire different bio-potentials, including EEG, EMG, ECG. Module performance is very good (RTI noise < 1.3 uVpp), and was favourably compared against a commercial device (g.tec USBamp). The device was integrated into an ad-hoc developed Matlab-based platform, which handles the hardware control, as well as the data streaming, logging and processing. Via a specifically developed plug-in, incoming data can also be streamed to a TOBI-interface compatible system. As a demonstrator, the BCI was developed for AAL (Ambient Assisted Living) system-control purposes, having in mind the following requirements: (i) online, self-paced BCI operation (i.e., the BCI monitors the EEG in real-time and must discern between intentional control periods, and non-intentional, rest ones, interpreting the user’s intent only in the first case); (ii) calibration-free approach (“ready-to-use”, “Plug&Play”); (iii) subject-independence (general approach). The choice of the BCI operating paradigm fell on Steady State visual Evoked Potential (SSVEP). Two offline SSVEP classification algorithms were proposed and compared against reference literature, highlighting good performance, especially in terms of lower computational complexity. A method for improving classification accuracy was presented, suitable for use in online, self-paced scenarios (since it can be used to discriminate between intentional control periods and non-intentional ones). Results show a very good performance, in particular in terms of false positives immunity (0.26 min^-1), significantly improving over the state of the art. The whole BCI setup was tested both in lab condition, as well as in relatively harsher ones (in terms of environmental noise and non-idealities), such as in the context of the Handimatica 2014 exhibition. In both cases, a demonstrator allowing control of home appliances through BCI was developed

Integrated Optical Amplifier–Photodetector on a Wearable Nanocellulose Substrate

Flexible optoelectronics has emerged as an outstanding platform to pave the road toward vanguard technology advancements. As compared to conventional rigid substrates, a flexible technology enables mechanical deformation while maintaining stable performance. The advantages include not only the development to novel applications, but also the implementation of a wearable technology directly in contact with a curved surface. Here the monolithic integration of a perovskite‐based optical waveguide amplifier together with a photodetector on a nanocellulose substrate is shown to demonstrate the feasibility of a stretchable signal manipulation and receptor system fabricated on a biodegradable material. An integrated optical amplifier–photodetector is developed in which the photocurrent is exploited that is generated in the organic–inorganic lead halide perovskite under an applied bias. Such photocurrent does not minimally perturb the amplifier operation and is used to monitor the light signal propagating along the waveguide, opening a broad range of applications for example to regulate the operation temperature

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

Cloud-Based Behavioral Monitoring in Smart Homes

Environmental sensors are exploited in smart homes for many purposes. Sensor data inherently carries behavioral information, possibly useful to infer wellness and health-related insights in an indirect fashion. In order to exploit such features, however, powerful analytics are needed to convert raw sensor output into meaningful and accessible knowledge. In this paper, a complete monitoring architecture is presented, including home sensors and cloud-based back-end services. Unsupervised techniques for behavioral data analysis are presented, including: (i) regression and outlier detection models (also used as feature extractors for more complex models); (ii) statistical hypothesis testing frameworks for detecting changes in sensor-detected activities; and (iii) a clustering process, leveraging deep learning techniques, for extracting complex, multivariate patterns from daily sensor data. Such methods are discussed and evaluated on real-life data, collected within several EU-funded projects. Overall, the presented methods may prove very useful to build effective monitoring services, suitable for practical exploitation in caregiving activities, complementing conventional telemedicine techniques

A multi-modal BCI system for active and assisted living

Brain Computer Interface (BCI) technology is an alternative/augmentative communication channel, based on the interpretation of the user’s brain activity, who can then interact with the environment without relying on neuromuscular pathways. BCI can thus be placed in the context of human-machine interfaces and, considering a possible application scenario to smart homes, they can serve as a technological bridge to make Active and Assisted Living (AAL) systems’ functionalities accessible to subjects who would not otherwise be able to actively use. In this paper, BCI is specifically conceived for AAL system control experience, developed ad-hoc, considering cost and compactness constraints, besides classification performance. The implemented solution is quite general, as it can handle multiple bio-potentials: this feature allows to exploit different information channels, namely, ElectroEncephaloGraphy (EEG) and ElectroMyoGraphy (EMG). Each subsystem (EEG, EMG) is presented and its performance discussed; both can operate in real-time and in self-paced mode (i.e. they automatically recognize if a command is being issued and, in this case, which one). In particular, the EEG part, based on Steady State Visual-Evoked Potentials (SSVEP), can achieve very good results in terms of false positive rejection, improving over the state of the art (False Positive Rate: 0.16 min−1). Moreover, such results are achieved without any initial system calibration phase. Meanwhile, the EMG subsystem can be used as a smart switch for SSVEP stimuli control (on/off), improving user’s comfort when no control periods are desired

Hybrid BCI systems as HCI in ambient assisted living scenarios

Brain Computer Interface (BCI) technology is an alternative/ augmentative communication channel, based on the interpretation of the user’s brain activity, who can then interact with the environment without relying on neuromuscular pathways. Such technologies can act as alternative HCI devices towards AAL (Ambient Assisted Living) systems, thus opening their services to people for whom interacting with conventional interfaces could be troublesome, or even not viable. A complete BCI implementation is presented and discussed, briefly introducing the customized hardware and focusing more on the signal processing aspects. The BCI is based on SSVEP signals, featuring self-paced calibration-less operation, aiming at a “plug&play” approach. The signal processing chain is presented, introducing a novel method for improving accuracy and immunity to false positives. The results achieved, especially in terms of false positive rate containment (0.16 min−1) significantly improve over the literature. In addition, a possible integration of EMG signals in a hybrid-BCI scheme is discussed, serving as a binary switch to turn on/off the EEG-based BCI section (and the flashing stimuli unit). This can have positive impact on both the user’s comfort as well as on the resilience towards false positives. Preliminary results for jaw clench recognition show good detectability, proving that such integration can be implemented

Simple spice based modeling platform for 4.5 kV power IGBT modules

The paper deals with the development of a PSpice based modeling platform for the evaluation of 4.5 kV and 2.0 kA power IGBT modules to be used in HVDC and FACTS applications. Using PSpice, a set of device parameters (both for IGBTs and diodes) have first been extracted and verified by static and dynamic comparison of experimental data from 4.5 kV and 2.0 kA Si based power modules. Implemented device models along with complete gate driver circuitry unit in PSpice show fair agreement with experimental dynamic results and present realistic prediction of losses under various operating conditions (i.e., voltage and current ratings, stray inductances, gate resistances, temperature, variations in gate currents and gate voltages etc). The modeling platform, thus, supports not only the performance prediction of converter cell design but also possibly avoids the usage of extensive laboratory testing

A Unified Methodology for Heartbeats Detection in Seismocardiogram and Ballistocardiogram Signals

This work presents a methodology to analyze and segment both seismocardiogram (SCG) and ballistocardiogram (BCG) signals in a unified fashion. An unsupervised approach is followed to extract a template of SCG/BCG heartbeats, which is then used to fine-tune temporal waveform annotation. Rigorous performance assessment is conducted in terms of sensitivity, precision, Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) of annotation. The methodology is tested on four independent datasets, covering different measurement setups and time resolutions. A wide application range is therefore explored, which better characterizes the robustness and generality of the method with respect to a single dataset. Overall, sensitivity and precision scores are uniform across all datasets ( p &gt; 0.05 from the Kruskal&ndash;Wallis test): the average sensitivity among datasets is 98.7%, with 98.2% precision. On the other hand, a slight yet significant difference in RMSE and MAE scores was found ( p &lt; 0.01 ) in favor of datasets with higher sampling frequency. The best RMSE scores for SCG and BCG are 4.5 and 4.8 ms, respectively; similarly, the best MAE scores are 3.3 and 3.6 ms. The results were compared to relevant recent literature and are found to improve both detection performance and temporal annotation errors

A Simplified Model for SiC Power Diode Modules for Implementation in Spice Based Simulators

A simplified model for SiC Power diodes has been developed and implemented in Spice simulator in order to get the advantages of a modular and hierarchical structure that can be easily used for modeling of power semiconductor modules. The proposed approach is based on the lumped charge technique. One of the main targets for the proposed model is the implementation of a modeling structure starting from the evaluation and simplification of the semiconductor equations. The paper will show the implementation of the model along with an experimental evaluation of the proposed method