1,720,975 research outputs found
A Driving Right Leg Circuit (DgRL) for Improved Common Mode Rejection in Bio-Potential Acquisition Systems
Full text linkThe paper presents a novel Driving Right Leg (DgRL) circuit designed to mitigate the effect of common mode signals deriving, say, from power line interferences. The DgRL drives the isolated ground of the instrumentation towards a voltage which is fixed with respect to the common mode potential on the subject, therefore minimizing common mode voltage at the input of the front-end. The paper provides an analytical derivation of the common mode rejection performances of DgRL as compared to the usual grounding circuit or Driven Right Leg (DRL) loop. DgRL is integrated in a bio-potential acquisition system to show how it can reduce the common mode signal of more than 70 dB with respect to standard patient grounding. This value is at least 30 dB higher than the reduction achievable with DRL, making DgRL suitable for single-ended front-ends, like those based on active electrodes. EEG signal acquisition is performed to show how the system can successfully cancel power line interference without any need for differential acquisition, signal post-processing or filtering
Active Electrode IC for EEG and Electrical Impedance Tomography with Continuous monitoring of contact impedance
Full text linkThe IC presented integrates the front-end for EEG and Electrical Impedance Tomography (EIT) acquisition on the electrode, together with electrode-skin contact impedance monitoring and EIT current generation, so as to improve signal quality and integration of the two techniques for brain imaging applications. The electrode size is less than 2 cm(2) and only 4 wires connect the electrode to the back-end. The readout circuit is based on a Differential Difference Amplifier and performs single-ended amplification and frequency division multiplexing of the three signals that are sent to the back-end on a single wire which also provides power supply. Since the system's CMRR is a function of each electrode's gain accuracy, an analysis is performed on how this is influenced by mismatches in passive and active components. The circuit is fabricated in 0.35 μm CMOS process and occupies 4 mm(2), the readout circuit consumes 360 μW, the input referred noise for bipolar EEG signal acquisition is 0.56 μVRMS between 0.5 and 100 Hz and almost halves if only EEG signal is acquired
An accurate low-cost Crackmeter with LoRaWAN communication and energy harvesting capability
No full textStructural health monitoring (SHM) systems are becoming increasingly widespread and are in some cases mandated by law. A major factor limiting the diffusion of such systems is the lack of low-cost low-power sensor nodes, which can be deployed in large numbers in hard-to-reach areas, while providing high-quality precise measurements over their entire lifespan with minimum maintenance and withstanding climatic stress. In this paper, we present a cost-effective wireless component for Structural Health Monitoring (SHM) that measure and track cracks in concrete and other construction materials. The sensor combines a microprocessor with LoRaWAN wireless communication, an analog transducer, and a solar energy harvester, allowing long-term remote monitoring with easy plug and play installation. Experimental results demonstrate that we achieved about 1\mu\mathrmm accuracy and an expected lifetime of more than 10 years, with stable measurements across a-IS-65°C temperature range
Parametric Detection and Classification of Compact Conductivity Contrasts With Electrical Impedance Tomography
Full text linkElectrical impedance tomography is a noninvasive and cost-effective imaging method that is increasingly attractive in the field of medical diagnostics. Several health conditions, such as stroke and solid tumors, are characterized by compact conductivity anomalies surrounded by a fairly regular background. Commonly employed voxel-by-voxel reconstruction methods for impedance imaging share the disadvantages of high computational cost and substantial sensitivity to measurement noise and imperfections in the electrical model describing the domain of interest. We present a special purpose algorithm for automatic detection and identification of compact conductivity variations. The technique exploits a priori structural information and, by reconstructing only the limited number of parameters required to describe a compact conductivity contrast, does not depend on a critical regularization parameter. The most demanding kernels are implemented to run on graphics processing units to accelerate computation. The parametric reconstruction is quicker and more robust than widely employed approaches with respect to measurement noise and imperfections in the electrical model, as shown by computational analysis performed on a segmented head domain and experimental measurements acquired on a cylindrical phantom. When the goal is quick detection of compact conductivity contrasts in complex 3-D domains, the inclusion of specific constraints relating to the problem considered leads to enhanced quality of reconstruction, making the presented technique a promising alternative to common voxel-by-voxel reconstruction methods
EEG acquisition system based on active electrodes with common-mode interference suppression by Driving Right Leg circuit
No full textWe present a system for the acquisition of EEG signals based on active electrodes and implementing a Driving Right Leg circuit (DgRL). DgRL allows for single-ended amplification and analog-to-digital conversion, still guaranteeing a common mode rejection in excess of 110 dB. This allows the system to acquire high-quality EEG signals essentially removing network interference for both wet and dry-contact electrodes. The front-end amplification stage is integrated on the electrode, minimizing the system's sensitivity to electrode contact quality, cable movement and common mode interference. The A/D conversion stage can be either integrated in the remote back-end or placed on the head as well, allowing for an all-digital communication to the back-end. Noise integrated in the band from 0.5 to 100 Hz is comprised between 0.62 and 1.3 μV, depending on the configuration. Current consumption for the amplification and A/D conversion of one channel is 390 μA. Thanks to its low noise, the high level of interference suppression and its quick setup capabilities, the system is particularly suitable for use outside clinical environments, such as in home care, brain-computer interfaces or consumer-oriented applications
Creamino: a Cost-Effective, Open-Source EEG-based BCI System
Full text linkThis paper presents an open source framework called Creamino. It consists of an Arduino-based cost-effective quick-setup EEG platform built with off-the shelf components and a set of software modules that easily allow users to connect this system to Simulink or BCI-oriented tools (such as BCI2000 or OpenViBE) and set up a wide number of neuroscientific experiments. Creamino is capable of processing multiple EEG channels in real-time and operates under Windows, Linux and Mac OS X in real-time on a standard PC. Its objective is to provide a system that can be readily fabricated and used for neurophysiological experiments and, at the same time, can serve as the basis for development of novel BCI platforms by accessing and modifying its open source hardware and software libraries. Schematics, gerber files, bill of materials, source code, software modules, demonstration videos and instructions on how to use these modules are available free of charge for research and educational purposes online at https://github.com/ArcesUnibo/creamino. Application cases show how the system can be used for neuroscientific or BCI experiments. Thanks to its low production cost and its compatibility with open-source BCI tools, the system presented is particularly suitable for use in BCI research and educational applications
BioGAP: a 10-Core FP-capable Ultra-Low Power IoT Processor, with Medical-Grade AFE and BLE Connectivity for Wearable Biosignal Processing
No full textWearable biosignal processing applications are driving significant progress toward miniaturized, energy-efficient Internet-of- Things solutions for both clinical and consumer applications. However, scaling toward high-density multi-channel front-ends is only feasible by performing data processing and machine Learning (ML) near-sensor through energy-efficient edge processing. To tackle these challenges, we introduce BioGAP, a novel, compact, modular, and lightweight (6g) medical-grade biosignal acquisition and processing platform powered by GAP9, a ten-core ultra-low-power SoC designed for efficient multi-precision (from FP to aggressively quantized integer) processing, as required for advanced ML and DSP. BioGAP's form factor is 16x21x14 mm 3 and comprises two stacked PCBs: a baseboard integrating the GAP9 SoC, a wireless Bluetooth Low Energy (BLE) capable SoC, a power management circuit, and an accelerometer; and a shield including an analog front-end (AFE) for ExG acquisition. Finally, the system also includes a flexibly placeable photoplethysmogram (PPG) PCB with a size of 9x7x3 mm 3 and a rechargeable battery ( φ 12x5 mm 2 ), We demonstrate BioGAP on a Steady State Visually Evoked Potential (SSVEP)-based Brain-Computer Interface (BCI) application. We achieve 3.6 μJ/sample in streaming and 2.2 μJ/sample in onboard processing mode, thanks to an efficiency on the FFT computation task of 16.7 Mflops/s/mW with wireless bandwidth reduction of 97%, within a power budget of just 18.2 mW allowing for an operation time of 15
Parallel Solver for Diffuse Optical Tomography on Realistic Head Models with Scattering and Clear Regions
No full textDiffuse optical tomography is an imaging technique, based on evaluation of how light propagates within the human head to obtain the functional information about the brain. Precision in reconstructing such an optical properties map is highly affected by the accuracy of the light propagation model implemented, which needs to take into account the presence of clear and scattering tissues. We present a numerical solver based on the radiosity-diffusion model, integrating the anatomical information provided by a structural MRI. The solver is designed to run on parallel heterogeneous platforms based on multiple GPUS and CPUs. We demonstrate how the solver provides a 7 times speed-up over an isotropic-scattered parallel Monte Carlo engine based on a radiative transport equation for a domain composed of 2 million voxels, along with a significant improvement in accuracy. The speed-up greatly increases for larger domains, allowing us to compute the light distribution of a full human head ( approx 3 million voxels) in 116 s for the platform used
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
- …
