Integrated Wireless Neural Recording System for

Neuroprosthetics and Advanced Neuroscience Research

Moo Sung Chae

Integrated Bioelectronics Group Department of Electrical Engineering University of California, Santa Cruz

Overview

  Neural Recording Systems

  Motivations

  Design Issues

  Noise

  Optimization

  Wireless

  Prototype Systems and Test Results

  Summary

  A recording system to monitor neuron’s activities

  Neurons communicate by “action potentials”

  Action potential is an all-or-nothing signal

Neural Recording Systems

Near field signal 1)   Intra-cellular action potential : rapid change in trans membrane

potential cause by the voltage dependent trans membrane conductance (H-H model)

2)   Extra-cellular action potential : solenoidal current around neurons, few hundred µm

3)   Local field potential (LFP) : coherent low frequency change, a few mm

Far field signal 1)   Electrocorticograms (EcoG) : sub-dural

2)   Electroencephalograph (EEG) : on the scalp

Neural Signals

Signal Type Bandwidth (Hz) Range (mVpp)

EEG 0.05 ~ 128 0.02 ~ 0.4

ECoG 0.1 ~ 64 0.02 ~ 1

EMG 1 ~ 128 0.02 ~ 1

LFPs 0.1 ~ 100 0.1 ~ 1

EAPs 100 ~ 10K 0.04 ~ 0.2

IAPs 100 ~ 10K ~ 100

Neural Signals – Properties

Adjustability of the amplifier is necessary

  Study of complex neural networks of the animals in their natural environment

  A large number of recording channels

  To remove tethering wire

  Miniaturized and low power consumption

Motivations (1) – Advanced Neuroscience

Artificial devices to replace or improve the function of an impaired nervous system

 Upper and lower limb prosthesis [3],[4]

  Bladder and bowel movement [5],[6]

  Respiration control for SCI patients [7]

  Hand grasping [8]

Motivations (2) – Neural Prostheses

Challenges

  Simultaneous recording from a large number of channels (more than 100 channels) [9]

  Untethered wireless transmission of recorded data in free running animals

  On-the-fly spike sorting

  Programmability and Versatility

  Integration and Miniaturization

  Low-power consumption

Proposed System

  Design methodology to optimize the power and area

  Programmable gain and bandwidth of the amplifiers

  On-the-fly spike sorting engine

  High data rate & Low power transmitter for simultaneous recording of 128 channels (90 Mbps)

Front-end Blocks

Decide the resolution of ADC

Optimize preamplifier

Decide the number of channel per one ADC

System resolution by electrode noise

Proposed Architecture

Component modeling

1. Fixed design constants

2. Design variables to optimize

Design Methodology

Noise Sources

Neuronal Noise

~ 20µVrms

Tissue/Electrode Noise

~ 10µVrms

Electronics Noise

~ 5µVrms

Z. Yang, Q. Zhao, E. Keefer and W. Liu, "Noise Characterization, Modeling, and Reduction for In Vivo Neural Recording," to appear in Neural Information Processing Systems (NIPS), 2010.

  Biological and interface noise is larger than circuit noise.

  Need to optimize the performance of the amplifiers

  Different techniques for different noise sources

Neural Signal Processing Engine

UWB

1. FCC assigned 3.1 ~ 10.6 GHz spectrum for unlicensed medical and communication systems.

2. Short range applications with large bandwidth 3. Small antenna size due to high frequency nature

IR UWB vs Multi-carrier UWB

  Impulse radio (IR) UWB is more suitable than

multi-carrier UWB such as OFDM.

  Use UWB pulse for data symbol (telegraph)

  Simple CMOS pulse generator for Tx

  Low power at Tx, but high power at Rx

UWB Wireless Tx & Rx

H L H L L H H H L

10 01 10 01 01 10 10 10 01 00 00 00 00 00 00 00

serialized 9-bit sampled data Redundant data UWB pulse(PPM)

Manchester coded data

sampled data

UWB pulse(OOK)

UWB Pulse generator

Pulse shaping

Filter

UWB antenna

Encoder

OOK modulator

PPM modulator

dCLK pCLK

From ADC

MU

X

Pad & package parastics

9

Enc

oded

da

ta

OOKin

PPMin

Seriallizer

data

dCLK Mode sel

BPF @ 4 GHz LPF @ 100 MHz

LNA stage Diode Lowpass Filter

FPGA

Analog . Amplifier BPF UWB

Antennas

UWB Prototype

1. Custom designed TX chip in 0.35µm CMOS process 2. Manchester coding for clock recovery and channel

separation@RX 3. PPM and OOK 4. Adjustable pulse width 5. Receiver is implemented using commercial components.

UWB Prototype Test Results

[Output of CMOS TX] [PSD of the UWB pulse@TX]

[Recovered data@RX]

  Maximum data rate of 90Mbps

  1mW power consumption with 90Mbps OOK

  Digital clock recovery by the FPGA in the RX

  USB interface with PC to handle high bandwidth

LPF output

FPGA output

FCC emission mask

Architecture of Prototype System

Bench Top Test Results

1. Ex-vivo extracellular action potential recording from a dissected snail brain (A circumoesophageal ring dissected from a H. Aspersa)

2.  Concentric bipolar electrode with 25µm platinum tip with suction

3.  Measured impedance of electrode is 190KΩ at 1KHz.

Ex-vivo Recording

In-vitro Recording

1. Snail neurons were cultured on Ayanda MEAs 2. Electrode impedance is 1MΩ. 3. Recording from a cluster of neurons 4. Field potentials were observed.

1.  4-channel recording IC (cut-down version of 128ch recording IC)

2.  EEG, ECG, EMG

3.  USB2.0 PC interface

FPGA

USB2.0

FSK

FSK

PIC Microcontroller

Another Prototype System

[A mouse wearing the recording system. Photo was taken at Arizona State University]

1.  Recording electrode implanted in a live mice brain

2.  The impedance of the electrode is 100KΩ.

3.  Extracellular action potentials

In-vivo Recording

Human EEG and ECG Recording

Summary & Acknowledgement

  Need for versatile wireless recording systems for advanced neuroscience research and neuroprothetics

  Challenging requirements in terms of power and area

  Need various technologies for the implementation of the system

  An example of system implementation

  Chip fabrication by National Semiconductor