A cybersecure P300-based brain-to-computer interface against noise-based and fake P300 cyberattacks

In a progressively interconnected world where the internet of things (IoT), ubiquitous computing, and artificial intelligence are leading to groundbreaking technology, cybersecurity remains an underdeveloped aspect. This is particularly alarming for brain-to-computer interfaces (BCIs), where hackers can threaten the user’s physical and psychological safety. In fact, standard algorithms currently employed in BCI systems are inadequate to deal with cyberattacks. In this paper, we propose a solution to improve the cybersecurity of BCI systems. As a case study, we focus on P300-based BCI systems using support vector machine (SVM) algorithms and EEG data. First, we verified that SVM algorithms are incapable of identifying hacking by simulating a set of cyberattacks using fake P300 signals and noise-based attacks. This was achieved by comparing the performance of several models when validated using real and hacked P300 datasets. Then, we implemented our solution to improve the cybersecurity of the system. The proposed solution is based on an EEG channel mixing approach to identify anomalies in the transmission channel due to hacking. Our study demonstrates that the proposed architecture can successfully identify 99.996% of simulated cyberattacks, implementing a dedicated counteraction that preserves most of BCI functions

Factor VIII companion diagnostic for haemophilia

Haemophilia is predominantly an inherited disorder that impairs the body’s ability to make blood clots, a process needed to stop bleeding. The condition of this disease is complex to manage, but many patients do so through home therapy and often only see their core multidisciplinary healthcare team annually. There is an increasing need for patients to be able to monitor their condition efficiently at home while staying connected with their healthcare team. As a consequence, a low-cost handheld self-monitoring solution for clotting factor is required. Here we have demonstrated a suitable one-step Factor VIII companion diagnostic sensing approach based on a chromogenic assay for haemophilia A. The results show comparable performance to the gold standard method. Our approach is able to deliver accurate cost-effective results in under 5 min from undiluted human plasma. It has the potential to be able to reduce the human and monetary costs of over- or under-medication for haemophiliacs

Noise characteristics with CMOS sensor array scaling

An important consideration when scaling semiconductor sensor devices is the effect it may have on noise performance. Overall signal to noise ratio can be improved both by increasing sensor size, or alternatively by averaging the signal from two or more smaller sensors. In the design of sensor systems it is not immediately clear which is the best strategy to pursue. In this paper, we present a detailed theoretical and experimental study based on three different sensor arrays that show that an array of small independent sensors is always less noisy than a large sensor of the same size

Capsule endoscopy compatible fluorescence imager demonstrated using bowel cancer tumours

We demonstrate a proof of concept highly miniaturised fluorescence imager and its application to detecting cancer in resected human colon cancer tissues. Fluorescence imaging modalities have already been successfully implemented in traditional endoscopy. However, the procedure still causes discomfort and requires sedation. Wireless fluorescence capsule endoscopy has the potential to improve diagnostic accuracy with less inconvenience for patients. In this paper we present a 5 mm x 6 mm x 5 mm optical block that is small enough to integrate into a capsule endoscope. The block integrates ultrathin filters for optical isolation and was successfully integrated with a sensitive CMOS SPAD array to detect green fluorescence from Flavin Adenine Dinucleotide (FAD), which is an endogenous fluorophore responsible for autofluorescence in human tissues, and fluorescence from the cancer selective molecular probe ProteoGREENTM-gGlu used to label colorectal cancer cells. In vitro studies were validated using a commercial ModulusTM Microplate reader. The potential use of the device in capsule endoscopy was further validated by imaging healthy and malignant resected human tissues from the colon to detect changes in autofluorescence signal that are crucial for cancer diagnosis

The Multicorder: A Handheld Multimodal Metabolomics-on-CMOS Sensing Platform

The use of CMOS platforms in medical point-of-care applications, by integrating all steps from sample to data output, has the potential to reduce the diagnostic cost and the time from days to seconds. Here we present the `Multicorder' technology, a handheld versatile multimodal platform for rapid metabolites quantification. The current platform is composed of a cartridge, a reader and a graphic user interface. The sensing core of the cartridge is the CMOS chip which integrates a 16×16 array of multi-sensor elements. Each element is composed of two optical and one chemical sensor. The platform is therefore capable of performing multi-mode measurements: namely colorimetric, chemiluminescence, pH sensing and surface plasmon resonance. In addition to the reader that is employed for addressing, data digitization and transmission, a tablet computer performs data collection, visualization, analysis and storage. In this paper, we demonstrate colorimetric, chemiluminescence and pH sensing on the same platform by on-chip quantification of different metabolites in their physiological range. The platform we have developed has the potential to lead the way to a new generation of commercial devices in the footsteps of the current commercial glucometers for quick multi-metabolite quantification for both acute and chronic medicines

Multimodal integrated sensor platform for rapid biomarker detection

Precision metabolomics and quantification for cost-effective, rapid diagnosis of disease are key goals in personalized medicine and point-of-care testing. Presently, patients are subjected to multiple test procedures requiring large laboratory equipment. Microelectronics has already made modern computing and communications possible by integration of complex functions within a single chip. As More than Moore technology increases in importance, integrated circuits for densely patterned sensor chips have grown in significance. Here, we present a versatile single CMOS chip forming a platform to address personalized needs through on-chip multimodal optical and electrochemical detection that will reduce the number of tests that patients must take. The chip integrates interleaved sensing subsystems for quadruple-mode colorimetric, chemiluminescent, surface plasmon resonance and hydrogen ion measurements. These subsystems include a photodiode array and a single photon avalanche diode array, with some elements functionalized to introduce a surface plasmon resonance mode. The chip also includes an array of ion sensitive field effect transistors. The sensor arrays are distributed uniformly over an active area on the chip surface in a scalable and modular design. Bio-functionalization of the physical sensors yields a highly selective simultaneous multiple-assay platform in a disposable format. We demonstrate its versatile capabilities through quantified bioassays performed on-chip for glucose, cholesterol, urea and urate, each within their naturally occurring physiological range

Edible Electronics and Robofood: A Move Towards Sensors for Edible Robots and Robotic Food

Sustainability remains an underdeveloped aspect when designing an electronic device, even though technology is more pervasive in our society. As such, a paradigm change is needed toward the use of more environmentally friendly materials and processes. Edible electronics proposes integrating food-grade materials into more complex system such as robots, thus contributing to reducing e-waste accumulation. Besides sustainability, biocompatibility, and biodegradability, the use of food-grade materials in electronics has unprecedented advantages including minimal toxicity levels, especially in case of ingestion. Thus, edible electronics and robotics opens unprecedented scenarios: in the future rescue drones could integrate edible components, effectively increasing the food payload of the mission; robotic food could be employed as drug delivery vectors for wild animals; ultimately, miniaturized edible robots could enable novel diagnostic tools that can be digested by the body after performing a specific test. The EU-funded “Robofood” project works towards this vision. In this frame, we present a versatile fully edible electrically conductive ink for edible electronics and robotics. The ink is based on activated carbon - an organic edible electronic conductor with a daily intake up to three orders of magnitude higher than metals - and is formulated to be deposited by spray coating. Successful deposition on different edible substrates was obtained. As a proof-of-principle for the use of this material in edible robotics, a first application for bending sensing is herein reported. The coating was interfaced with a standard microcontroller and data was recorded during finger bending. The materials and methods developed in this work have a high degree of versatility and could be applied to other scenarios. We believe that the vision supported by this project has the potential to open the way for novel edible technologies for applications such as medicine and food quality monitoring among others

The Truth Machine of Involuntary Movement: FPGA Based Cortico-muscular Analysis for Fall Prevention

Voluntary movements are managed by movement related potentials (MRPs) which are brain activity patterns detectable even 500ms before the movement itself. The cortico-muscular matching between brain (EEG) and muscles (EMG) activity allows the assessment of the intentionality of the performed movement. Basing on this knowledge, a real-time algorithm for falling risk prediction based on EMG/EEG coupled analysis is presented. The system architecture involves 8 EMG (limbs) and 8 EEG (motor-cortex) channels wirelessly collected by a FPGA (gateway) that contextually performs the real-time processing based on an event triggered time-frequency approach. The digital architecture is validated on the FPGA to determine resources utilization, related timing constraints and performance figures of a dedicated real-time ASIC implementation for wearable applications. The system resource utilization is 85.95% ALMs, 43283 ALUTs, 73.0% registers, 9.9% block memory of an Altera Cyclone V FPGA. The processing latency is lower than 1ms and the output are available in 56ms, respecting the time limit of 300ms. Outputs enables decision-taking for feedback delivering

Gait Analysis for Fall Prediction Using EMG Triggered Movement Related Potentials

Abnormal gait is an usual feature in neurodegenerative disease (i.e.: Huntington Chorea, Parkinson and Alzheimer), while the capability to maintain a stable posture and fluid walking is progressive impaired in aging. Monitoring and correcting the insurgence of abnormal dynamic balance opens new scenarios in the cure of these diseases and falls prevention. In this work, we present a study based on EEG time-frequency analysis to identify the correlation between synchronized EEG and EMG signals for gait analysis. Several tools for gait analysis are developed and experimented i.e. EMG trigger generation with dynamic threshold, EMG co-contraction, EEG movement related potentials (MRPs) and EEG event related desynchronizations (ERDs). This work particularly focus on gait analysis indexes implementation and experimentally obtained results based on a large dataset, including different type of gait i.e. normal gait, perturbed gait and gait during a second cognitive task (DT). A weighted average on the calculated indexes are exploited to quantify the falling risk

On-Line Shelf-Life Prediction in Perishable Goods Chain Through the Integration of WSN Technology With a 1st Order Kinetic Model

The improvements in sensors and wireless technology offer an effective way to enhance food safety and certification along all the perishable goods supply-chain, in order to reduce food waste and losses, while guaranteeing a high degree of quality and preventing diseases directly related to the use of expired or harmful products. In this paper, a complete system for continuous environmental parameters (i.e. temperature, light exposition and relative humidity) acquisition and real-time shelf-life prediction of monitored product is proposed. An algorithm based on a 1 st order kinetic model of the product quality decay with a variation rate evaluated accordingly to the Arrhenius law is proposed. A case study is also shown, i.e.: data during the storage phase of agricultural product (tomatoes) have been acquired through a wireless sensor networks and uploaded to a cloud service. The collected data, a sample per 15 minutes, are processed by the computation algorithm implemented on laptop: the overall delay due to data download and processing is just about 0,3 s. As consequence, the remaining shelf-life of the food can be estimated with a 5% uncertainty with a 2K temperature sensor, highlighting critical situation in the manufacturing environment and allowing timely intervention