A Wireless System for EEG Acquisition and Processing in an Earbud Form Factor with 600 Hours Battery Lifetime.

In recent years, in-ear electroencephalography (EEG) was demonstrated to record signals of similar quality compared to standard scalp-based EEG, and clinical applications of objective hearing threshold estimations have been reported. Existing devices, however, still lack important features. In fact, most of the available solutions are based on wet electrodes, require to be connected to external acquisition platforms, or do not offer on-board processing capabilities. Here we overcome all these limitations, presenting an ear-EEG system based on dry electrodes that includes all the acquisition, processing, and connectivity electronics directly in the ear bud. The earpiece is equipped with an ultra-low power analog front-end for analog-to-digital conversion, a low-power MEMS microphone, a low-power inertial measurement unit, and an ARM Cortex-M4 based microcontroller enabling on-board processing and Bluetooth Low Energy connectivity. The system can stream raw EEG data or perform data processing directly in-ear. We test the device by analysing its capability to detect brain response to external auditory stimuli, achieving 4 and 1.3 mW power consumption for data streaming or on board processing, respectively. The latter allows for 600 hours operation on a PR44 zinc-air battery. To the best of our knowledge, this is the first wireless and fully self-contained ear-EEG system performing on-board processing, all embedded in a single earbud. Clinical relevance- The proposed ear-EEG system can be employed for diagnostic tasks such as objective hearing threshold estimations, outside of clinical settings, thereby enabling it as a point-of-care solution. The long battery lifetime is also suitable for a continuous monitoring scenario.

RF-Powered Low-Energy Sensor Nodes for Predictive Maintenance in Electromagnetically Harsh Industrial Environments.

This work describes the design, implementation, and validation of a wireless sensor network for predictive maintenance and remote monitoring in metal-rich, electromagnetically harsh environments. Energy is provided wirelessly at 2.45 GHz employing a system of three co-located active antennas designed with a conformal shape such that it can power, on-demand, sensor nodes located in non-line-of-sight (NLOS) and difficult-to-reach positions. This allows for eliminating the periodic battery replacement of the customized sensor nodes, which are designed to be compact, low-power, and robust. A measurement campaign has been conducted in a real scenario, i.e., the engine compartment of a car, assuming the exploitation of the system in the automotive field. Our work demonstrates that a one radio-frequency (RF) source (illuminator) with a maximum effective isotropic radiated power (EIRP) of 27 dBm is capable of transferring the energy of 4.8 mJ required to fully charge the sensor node in less than 170 s, in the worst case of 112-cm distance between illuminator and node (NLOS). We also show how, in the worst case, the transferred power allows the node to operate every 60 s, where operation includes sampling accelerometer data for 1 s, extracting statistical information, transmitting a 20-byte payload, and receiving a 3-byte acknowledgment using the extremely robust Long Range (LoRa) communication technology. The energy requirement for an active cycle is between 1.45 and 1.65 mJ, while sleep mode current consumption is less than 150 nA, allowing for achieving the targeted battery-free operation with duty cycles as high as 1.7%.

BioWolf: A Sub-10-mW 8-Channel Advanced Brain-Computer Interface Platform With a Nine-Core Processor and BLE Connectivity.

Advancements in digital signal processing (DSP) and machine learning techniques have boosted the popularity of brain-computer interfaces (BCIs), where electroencephalography is a widely accepted method to enable intuitive human-machine interaction. Nevertheless, the evolution of such interfaces is currently hampered by the unavailability of embedded platforms capable of delivering the required computational power at high energy efficiency and allowing for a small and unobtrusive form factor. To fill this gap, we developed BioWolf, a highly wearable (40 mm × 20 mm × 2 mm) BCI platform based on Mr. Wolf, a parallel ultra low power system-on-chip featuring nine RISC-V cores with DSP-oriented instruction set extensions. BioWolf also integrates a commercial 8-channel medical-grade analog-to-digital converter, and an ARM-Cortex M4 microcontroller unit (MCU) with bluetooth low-energy connectivity. To demonstrate the capabilities of the system, we implemented and tested a BCI featuring canonical correlation analysis (CCA) of steady-state visual evoked potentials. The system achieves an average information transfer rate of 1.46 b/s (aligned with the state-of-the-art of bench-top systems). Thanks to the reduced power envelope of the digital computational platform, which consumes less than the analog front-end, the total power budget is just 6.31 mW, providing up to 38 h operation (65 mAh battery). To our knowledge, our design is the first to explore the significant energy boost of a parallel MCU with respect to single-core MCUs for CCA-based BCI.

Creamino: A Cost-Effective, Open-Source EEG-Based BCI System.

This 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.

A Driving Right Leg Circuit (DgRL) for Improved Common Mode Rejection in Bio-Potential Acquisition Systems.

The 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.

Parallel Solver for Diffuse Optical Tomography on Realistic Head Models With Scattering and Clear Regions.

Diffuse 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 ( ≈ 3 million voxels) in 116 s for the platform used.

EEG acquisition system based on active electrodes with common-mode interference suppression by Driving Right Leg circuit.

We 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.

Active electrode IC for EEG and electrical impedance tomography with continuous monitoring of contact impedance.

The 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.

A four-shell diffusion phantom of the head for electrical impedance tomography.

A four-shell head phantom has been built and characterized. Its structure is similar to that of nonhomogeneous concentric shell domains used by numerical solvers that better approximate current distribution than phantoms currently used to validate electrical impedance tomography systems. Each shell represents a head tissue, namely, skin, skull, cerebrospinal fluid, and brain. A novel technique, which employs a volume conductive impermeable film, has been implemented to prevent ion diffusion between different agar regions without affecting current distribution inside the phantom. Comparisons between simulations and phantom measurements performed over four days are given to prove both the adherence to the model in the frequency range between 10 kHz and 1 MHz and its long-term stability.

EIT forward problem parallel simulation environment with anisotropic tissue and realistic electrode models.

Electrical impedance tomography (EIT) is an imaging technology based on impedance measurements. To retrieve meaningful insights from these measurements, EIT relies on detailed knowledge of the underlying electrical properties of the body. This is obtained from numerical models of current flows therein. The nonhomogeneous and anisotropic electric properties of human tissues make accurate modeling and simulation very challenging, leading to a tradeoff between physical accuracy and technical feasibility, which at present severely limits the capabilities of EIT. This work presents a complete algorithmic flow for an accurate EIT modeling environment featuring high anatomical fidelity with a spatial resolution equal to that provided by an MRI and a novel realistic complete electrode model implementation. At the same time, we demonstrate that current graphics processing unit (GPU)-based platforms provide enough computational power that a domain discretized with five million voxels can be numerically modeled in about 30 s.