5 research outputs found

    Bioelectronic Sensor Nodes for Internet of Bodies

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    Energy-efficient sensing with Physically-secure communication for bio-sensors on, around and within the Human Body is a major area of research today for development of low-cost healthcare, enabling continuous monitoring and/or secure, perpetual operation. These devices, when used as a network of nodes form the Internet of Bodies (IoB), which poses certain challenges including stringent resource constraints (power/area/computation/memory), simultaneous sensing and communication, and security vulnerabilities as evidenced by the DHS and FDA advisories. One other major challenge is to find an efficient on-body energy harvesting method to support the sensing, communication, and security sub-modules. Due to the limitations in the harvested amount of energy, we require reduction of energy consumed per unit information, making the use of in-sensor analytics/processing imperative. In this paper, we review the challenges and opportunities in low-power sensing, processing and communication, with possible powering modalities for future bio-sensor nodes. Specifically, we analyze, compare and contrast (a) different sensing mechanisms such as voltage/current domain vs time-domain, (b) low-power, secure communication modalities including wireless techniques and human-body communication, and (c) different powering techniques for both wearable devices and implants.Comment: 30 pages, 5 Figures. This is a pre-print version of the article which has been accepted for Publication in Volume 25 of the Annual Review of Biomedical Engineering (2023). Only Personal Use is Permitte

    A Closed-Loop Bidirectional Brain-Machine Interface System For Freely Behaving Animals

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    A brain-machine interface (BMI) creates an artificial pathway between the brain and the external world. The research and applications of BMI have received enormous attention among the scientific community as well as the public in the past decade. However, most research of BMI relies on experiments with tethered or sedated animals, using rack-mount equipment, which significantly restricts the experimental methods and paradigms. Moreover, most research to date has focused on neural signal recording or decoding in an open-loop method. Although the use of a closed-loop, wireless BMI is critical to the success of an extensive range of neuroscience research, it is an approach yet to be widely used, with the electronics design being one of the major bottlenecks. The key goal of this research is to address the design challenges of a closed-loop, bidirectional BMI by providing innovative solutions from the neuron-electronics interface up to the system level. Circuit design innovations have been proposed in the neural recording front-end, the neural feature extraction module, and the neural stimulator. Practical design issues of the bidirectional neural interface, the closed-loop controller and the overall system integration have been carefully studied and discussed.To the best of our knowledge, this work presents the first reported portable system to provide all required hardware for a closed-loop sensorimotor neural interface, the first wireless sensory encoding experiment conducted in freely swimming animals, and the first bidirectional study of the hippocampal field potentials in freely behaving animals from sedation to sleep. This thesis gives a comprehensive survey of bidirectional BMI designs, reviews the key design trade-offs in neural recorders and stimulators, and summarizes neural features and mechanisms for a successful closed-loop operation. The circuit and system design details are presented with bench testing and animal experimental results. The methods, circuit techniques, system topology, and experimental paradigms proposed in this work can be used in a wide range of relevant neurophysiology research and neuroprosthetic development, especially in experiments using freely behaving animals

    Integrating Bidirectional Brain-Computer Interfaces in Low-Voltage CMOS

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    Thesis (Ph.D.)--University of Washington, 2020Realizations of small-form factor, ultra-low power bidirectional brain-computer interfaces (BBCIs) will enable treatment of chronic neurophysiological disorders and allow new modes to investigate brain function. Neural stimulators have been shown to effectively alleviate the symptoms of various neurological disorders, and development of closed-loop bidirectional neural interfaces will increase therapy effectiveness by adapting to real-time measurements. This dissertation studies implementation of neural interface functionality in a single chip consuming minimal power and silicon area with two novel techniques: a. Time-multiplexed, mixed-signal artifact cancellation for simultaneous stimulation and sensing; b. Compact integrated stimulators with on-chip resonant charge pumps. The following paragraphs enumerate the two proposed techniques and their associated challenges and advantages. First, integrated artifact cancellation allows uninterrupted recording of neural signals during stimulation pulses in adjacent tissue. Existing low-frequency signals can be preserved, and an artifact-immune recording system can quantify the body’s immediate response to stimulation. Cancelling artifacts is complicated by the magnitude difference between stimulation pulses and neural signals of interest. Stimulation artifacts are several orders of magnitude larger than the upper dynamic range of typical recording systems, so a canceller requires specialized front-end electronics. Stimulus artifact cancellation has been demonstrated with digital adaptive filters interfacing with a switched-capacitor analog recording front-end. On cue from the stimulator, the adaptive filter learns the artifact shape based on recording output and subtracts the full stimulus artifact waveform from the recording input. The technique was first prototyped with an FPGA-based adaptive filter interfacing with standalone recording and stimulation chips. Later, the algorithm was optimized for power-efficient operation over multiple stimulation and recording channels. It was then integrated into a multi-channel bidirectional interface capable of cancelling artifacts from four independent stimulators on four recording channels. The power-efficient canceller was fabricated in the 65nm TSMC low-power CMOS process, allowing use of low-voltage supplies for the calculation back-end. This enabled 60dB of artifact suppression with a full-scale limit of ±125mV while only consuming 49nW per channel. Second, effectively stimulating neural tissue through low form-factor electrodes requires high voltages to drive current through large electrode impedances. These stimulation voltages often exceed the maximum voltage ratings of the high-density CMOS technologies desired for compact neural interfaces. Stacked charge pumps are often used to generate large voltages with multiple low-voltage stages, protecting CMOS electronics. Standard charge pump implementations pump charge with large capacitors at low frequencies to maintain power efficiency. In these cases, charge pump capacitor area dominates the system size. The proposed stimulator uses resonant clocking techniques to maintain efficiency with small charge pump capacitors clocked at high frequencies. An integrated inductor creates a resonant tank with the charge pump capacitors, compensating for switching losses in the circuit. This technique was demonstrated in a multi-channel BBCI chip. Four independent differential stimulators were integrated with a 64-channel recording system and the previously mentioned artifact cancellation back-end in a 4mm² 65nm CMOS chip. The stimulators source up to 2mA of stimulation current with a range of ±11V. The internal charge pumps supply power with a DC-DC efficiency of 38%, as compared to the possible 6% of a theoretical non-resonant topology of equal size

    Proceedings of the 2018 Canadian Society for Mechanical Engineering (CSME) International Congress

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    Published proceedings of the 2018 Canadian Society for Mechanical Engineering (CSME) International Congress, hosted by York University, 27-30 May 2018
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