Compressed Sensing of Extracellular Neurophysiology Signals: A Review

This article presents a comprehensive survey of literature on the compressed sensing (CS) of neurophysiology signals. CS is a promising technique to achieve high-fidelity, low-rate, and hardware-efficient neural signal compression tasks for wireless streaming of massively parallel neural recording channels in next-generation neural interface technologies. The main objective is to provide a timely retrospective on applying the CS theory to the extracellular brain signals in the past decade. We will present a comprehensive review on the CS-based neural recording system architecture, the CS encoder hardware exploration and implementation, the sparse representation of neural signals, and the signal reconstruction algorithms. Deep learning-based CS methods are also discussed and compared with the traditional CS-based approaches. We will also extend our discussion to cover the technical challenges and prospects in this emerging field.

An omnidirectional WPT platform for distributed fully implanted neural recording systems

Recent progress on human brain science requires developing advanced neural recording system to capture the activity of large neural populations accurately, across a large area of the brain, and over extended periods. Recently proposed distributed neural recording systems with numerous implanted devices require reliably energizing them wirelessly. Random distribution of these mm-sized implants and brain motion place them at different positions and orientations with respect to the power transmitter. Therefore, traditional wireless power transfer techniques fall short of reaching sufficient power for all implants simultaneously, rendering some implants nonfunctional. In this paper, a three-layer power transmitting array with three-phase coil excitation current is introduced, which is capable of producing omnidirectional and homogeneous magnetic field across the volume where the Rx coils are located. The individual coil dimensions in the array is optimized to improve the worst-case scenario in terms of homogeneity, which is further verified by the measurements using a scaled-up prototype system. The measurement results show that the minimum received voltages is improved from 0.34 V for 10-mm side-length hexagonal transmitting coil array to 0.83 V for the optimal case, i.e., 35 mm side-length hexagonal transmitting coil array.

A bioelectric neural interface towards intuitive prosthetic control for amputees

ObjectiveWhile prosthetic hands with independently actuated digits have become commercially available, state-of-the-art human-machine interfaces (HMI) only permit control over a limited set of grasp patterns, which does not enable amputees to experience sufficient improvement in their daily activities to make an active prosthesis useful.ApproachHere we present a technology platform combining fully-integrated bioelectronics, implantable intrafascicular microelectrodes and deep learning-based artificial intelligence (AI) to facilitate this missing bridge by tapping into the intricate motor control signals of peripheral nerves. The bioelectric neural interface includes an ultra-low-noise neural recording system to sense electroneurography (ENG) signals from microelectrode arrays implanted in the residual nerves, and AI models employing the recurrent neural network (RNN) architecture to decode the subject’s motor intention.Main resultsA pilot human study has been carried out on a transradial amputee. We demonstrate that the information channel established by the proposed neural interface is sufficient to provide high accuracy control of a prosthetic hand up to 15 degrees of freedom (DOF). The interface is intuitive as it directly maps complex prosthesis movements to the patient’s true intention.SignificanceOur study layouts the foundation towards not only a robust and dexterous control strategy for modern neuroprostheses at a near-natural level approaching that of the able hand, but also an intuitive conduit for connecting human minds and machines through the peripheral neural pathways.Clinical trialDExterous Hand Control Through Fascicular Targeting (DEFT). Identifier: NCT02994160.

Representation of edges, head direction, and swimming kinematics in the brain of freely-navigating fish

Like most animals, the survival of fish depends on navigation in space. This capacity has been documented in behavioral studies that have revealed navigation strategies. However, little is known about how freely swimming fish represent space and locomotion in the brain to enable successful navigation. Using a wireless neural recording system, we measured the activity of single neurons in the goldfish lateral pallium, a brain region known to be involved in spatial memory and navigation, while the fish swam freely in a two-dimensional water tank. We found that cells in the lateral pallium of the goldfish encode the edges of the environment, the fish head direction, the fish swimming speed, and the fish swimming velocity-vector. This study sheds light on how information related to navigation is represented in the brain of fish and addresses the fundamental question of the neural basis of navigation in this group of vertebrates.

The Argo: A 65,536 channel recording system for high density neural recording in vivo

Here we demonstrate the Argo System, a massively parallel neural recording system based on platinum-iridium microwire electrode arrays bonded to a CMOS voltage amplifier array. The Argo system is the highest channel count in vivo neural recording system built to date, supporting simultaneous recording from 65,536 channels, sampled at over 32 kHz and 12-bit resolution. This system is designed for cortical recordings, compatible with both penetrating and surface microelectrodes. We have validated this system by recording spiking activity from 791 neurons in rats and cortical surface Local Field Potential (LFP) activity from over 30,000 channels in sheep. While currently adapted for head-fixed recording, the microwire-CMOS architecture is well suited for clinical translation. Thus, this demonstration helps pave the way for a future high data rate intracortical implant.

Optimised Design and Implementation of Band-Pass Filter for Neural Recording System

In this paper, an eight order efficient digital infinite impulse response filter is designed to improve the signal to noise ratio (SNR) and minimise the hardware and power consumption. For this task, an optimisation method has been adapted to reduce the root mean square error and hardware usage. The filter has been designed and analysed using Matlab and Modelsim, the implementation has been synthesis on Xilinx Spartan 3E-100 (xc3s100e) field-programmable gate array board. Moreover, an optimisation process using parallel algorithm has bee adapted for further reduction in the hardware area and power consumption. The results show the Band Pass Filter effectively functions in real time recording application with significant improvement in the SNR which could achieve high-velocity selective resolution. The present work offers a structure of implementing a band-pass filter on FPGAs using a nonlinear digital filter shows a significant saving of 25.4% in power consumption and 29.9% of the hardware size comparing with the latest algorithm of IIR filter design. Consequently, this is an essential development to enhance the neural signals to be adopted as reference or control signals in artificial limbs devices.