134 research outputs found

    A self-calibration circuit for a neural spike recording channel

    Get PDF
    This paper presents a self-calibration circuit for a neural spike recording channel. The proposed design tunes the bandwidth of the signal acquisition Band-Pass Filter (BPF), which suffers from process variations corners. It also performs the adjustment of the Programmable Gain Amplifier (PGA) gain to maximize the input voltage range of the analog-to-digital conversion. The circuit, which consists on a frequency-controlled signal generator and a digital processor, operates in foreground, is completely autonomous and integrable in an estimated area of 0.026mm 2 , with a power consumption around 450nW. The calibration procedure takes less than 250ms to select the configuration whose performance is closest to the required one.Ministerio de Ciencia e InnovaciĂłn TEC2009-08447Junta de AndalucĂ­a TIC-0281

    Communication channel analysis and real time compressed sensing for high density neural recording devices

    Get PDF
    Next generation neural recording and Brain- Machine Interface (BMI) devices call for high density or distributed systems with more than 1000 recording sites. As the recording site density grows, the device generates data on the scale of several hundred megabits per second (Mbps). Transmitting such large amounts of data induces significant power consumption and heat dissipation for the implanted electronics. Facing these constraints, efficient on-chip compression techniques become essential to the reduction of implanted systems power consumption. This paper analyzes the communication channel constraints for high density neural recording devices. This paper then quantifies the improvement on communication channel using efficient on-chip compression methods. Finally, This paper describes a Compressed Sensing (CS) based system that can reduce the data rate by > 10x times while using power on the order of a few hundred nW per recording channel

    A 64-channel inductively-powered neural recording sensor array

    Get PDF
    This paper reports a 64-channel inductively powered neural recording sensor array. Neural signals are acquired, filtered, digitized and compressed in the channels. Additionally, each channel implements a local auto-calibration mechanism which configures the transfer characteristics of the recording site. The system has two operation modes; in one case the information captured by the channels is sent as uncompressed raw data; in the other, feature vectors extracted from the detected neural spikes are transmitted. Data streams coming from the channels are serialized by an embedded digital processor and transferred to the outside by means of the same inductive link used for powering the system. Simulation results show that the power consumption of the complete system is 377ÎŒW.Ministerio de Ciencia e InnovaciĂłn TEC2009-0844

    A neural probe with up to 966 electrodes and up to 384 configurable channels in 0.13 ÎŒm SOI CMOS

    Get PDF
    In vivo recording of neural action-potential and local-field-potential signals requires the use of high-resolution penetrating probes. Several international initiatives to better understand the brain are driving technology efforts towards maximizing the number of recording sites while minimizing the neural probe dimensions. We designed and fabricated (0.13-ÎŒm SOI Al CMOS) a 384-channel configurable neural probe for large-scale in vivo recording of neural signals. Up to 966 selectable active electrodes were integrated along an implantable shank (70 ÎŒm wide, 10 mm long, 20 ÎŒm thick), achieving a crosstalk of −64.4 dB. The probe base (5 × 9 mm2) implements dual-band recording and a 1

    Self-calibration of neural recording sensors

    Get PDF
    This paper reports a calibration system for automatically adjusting the bandpass and gain characteristics of programmable E×G sensors. The calibration mechanism of the bandpass characteristic is based on a mixed-signal tuning loop which uses as feedback signal the output of the data converter following the signal conditioning of the E×G sensor. Intended high-pass and low-pass frequency poles of the transfer function are injected into the loop by means of a direct frequency synthesizer followed by a smoothing atenuator.Ministerio de Ciencia e Innovación TEC2012-3363

    An implantable mixed-signal CMOS die for battery-powered in vivo blowfly neural recordings

    Get PDF
    © 2018 A mixed-signal die containing two differential input amplifiers, a multiplexer and a 50 KSPS, 10-bit SAR ADC, has been designed and fabricated in a 0.35 Όm CMOS process for in vivo neural recording from freely moving blowflies where power supplied voltage drops quickly due to the space/weight limited insufficient capacity of the battery. The designed neural amplifier has a 66 + dB gain, 0.13 Hz-5.3 KHz bandwidth and 0.39% THD. A 20% power supply voltage drop causes only a 3% change in amplifier gain and 0.9-bit resolution degrading for SAR ADC while the on-chip data modulation reduces the chip size, rendering the designed chip suitable for battery-powered applications. The fabricated die occupies 1.1 mm2 while consuming 238 ΌW, being suitable for implantable neural recordings from insects as small as a blowfly for electrophysiological studies of their sensorimotor control mechanisms. The functionality of the die has been validated by recording the signals from identified interneurons in the blowfly visual system

    Recent Advances in Neural Recording Microsystems

    Get PDF
    The accelerating pace of research in neuroscience has created a considerable demand for neural interfacing microsystems capable of monitoring the activity of large groups of neurons. These emerging tools have revealed a tremendous potential for the advancement of knowledge in brain research and for the development of useful clinical applications. They can extract the relevant control signals directly from the brain enabling individuals with severe disabilities to communicate their intentions to other devices, like computers or various prostheses. Such microsystems are self-contained devices composed of a neural probe attached with an integrated circuit for extracting neural signals from multiple channels, and transferring the data outside the body. The greatest challenge facing development of such emerging devices into viable clinical systems involves addressing their small form factor and low-power consumption constraints, while providing superior resolution. In this paper, we survey the recent progress in the design and the implementation of multi-channel neural recording Microsystems, with particular emphasis on the design of recording and telemetry electronics. An overview of the numerous neural signal modalities is given and the existing microsystem topologies are covered. We present energy-efficient sensory circuits to retrieve weak signals from neural probes and we compare them. We cover data management and smart power scheduling approaches, and we review advances in low-power telemetry. Finally, we conclude by summarizing the remaining challenges and by highlighting the emerging trends in the field

    System level design of a full-duplex wireless transceiver for brain-machine interfaces

    Get PDF
    We propose a new wireless communication architecture for implanted systems that simultaneously stimulates neurons and record neural responses. This architecture can support large numbers of electrodes (>500), providing 100 Mb/s for the downlink of stimulation signals, and gigabits per second for the uplink of neural recordings. We propose a full-duplex transceiver architecture that shares one antenna for both the ultrawideband (UWB) and the 2.45-GHz industrial, scientific, and medical band. A new pulse shaper is used for the gigabits per second uplink to simplify the transceiver design, while supporting several modulation formats with high data rates. To validate our system-level design for brain-machine interfaces, we present an ex-vivo experimental demonstration of the architecture. While the system design is for an integrated solution, the proof-of-concept demonstration uses discrete components. Good bit error rate performance over a biological channel at 0.5-, 1-, and 2-Gb/s data rates for uplink telemetry (UWB) and 100 Mb/s for downlink telemetry (2.45-GHz band) are achieved

    Neuromorphic robotic platform with visual input, processor and actuator, based on spiking neural networks

    Get PDF
    This paper describes the design and modus of operation of a neuromorphic robotic platform based on SpiNNaker, and its implementation on the goalkeeper task. The robotic system utilises an address event representation (AER) type of camera (dynamic vision sensor (DVS)) to capture features of a moving ball, and a servo motor to position the goalkeeper to intercept the incoming ball. At the backbone of the system is a microcontroller (Arduino Due) which facilitates communication and control between different robot parts. A spiking neuronal network (SNN), which is running on SpiNNaker, predicts the location of arrival of the moving ball and decides where to place the goalkeeper. In our setup, the maximum data transmission speed of the closed-loop system is approximately 3000 packets per second for both uplink and downlink, and the robot can intercept balls whose speed is up to 1 m/s starting from the distance of about 0.8 m. The interception accuracy is up to 85%, the response latency is 6.5 ms and the maximum power consumption is 7.15W. This is better than previous implementations based on PC. Here, a simplified version of an SNN has been developed for the ‘interception of a moving object’ task, for the purpose of demonstrating the platform, however a generalised SNN for this problem is a nontrivial problem. A demo video of the robot goalie is available on YouTube
    • 

    corecore