ELECTRONIC INTERFACE FOR NEURAL ACTIVITY

RECORDING AND STIMULATION

Gianmarco Angius, Caterina Carboni, Daniela Loi and Massimo Barbaro

Department of Electrical and Electronic Engineering, University of Cagliari, Piazza d’Armi, Cagliari, Italy

Keywords:

Neural extracellular recording, Neural Stimulation, Neural signal processing.

Abstract:

A portable neural activity acquisition and stimulation system by means of tfLIFE implantable electrodes has

been realized. The detecting circuit provides: a selective ﬁltering made up of a 4th order high pass Multiple

Feedback ﬁlter ( f

−3dB

= 1.1kHz) and a 4th order low pass Multiple Feedback ﬁlter ( f

−3dB

= 2kHz), a variable

gain (24dB - 44dB) and a 16 bit analog to digital conversion. The stimulator allows to generate speciﬁc

electrical signals through a digital-to-analog converter while stimulation parameters as frequency, duration

and intensity are controlled by a digital microcontroller. Simulation results and ﬁrst experimental results of

the interface demonstrate how neural signals of a few of microvolts can be ﬁltered, programmable ampliﬁed

and digitalized without distortion.

1 INTRODUCTION

In recent years there has been an explosion of in-

terest in the development of technologies whose end

goal is to analyze the human nervous system and in

particular, the correlation between the neural activ-

ity and speciﬁc cognitive, sensory and motor func-

tions (Wessberg et al., 2000; Salzman and Newsome,

1994). Recent developments in microelectronic sys-

tem technology have made easier the design of neural-

controlled interfaces by means of appropriate elec-

trodes which allow a selective link with the peripheral

nervous system (PNS). A major problem in such ap-

plications is the morphology of the signals to record.

As a matter of fact, extracellular neural signals (ENG)

are characterized by a low-amplitude signal (in the

order of microvolts) and low-frequency bandwidth

(main energy spectrum concentrated close to 2kHz).

The acquisition of this kind of signals is therefore

affected by noise, mainly due to electro-myographic

signals (EMG) interference and to biological envi-

ronment (Wang et al., 2005; Watkins et al., 2006).

EMG signals have an amplitude of the order of mil-

livolts (several orders of magnitude greater than use-

ful ones) but are limited in frequency below 300Hz.

Low-noise ampliﬁcation and selective ﬁltering repre-

sent then the two basic operations of any recording

circuit proposed in the past (Obeid et al., 2004; Gos-

selin and Sawan, 2005; Jochum et al., 2009). This

work presents the design and test of a low power elec-

tronic neural system which allows a bi-directional in-

teraction between the brain and “smart” artiﬁcial de-

vices. The main goal was to realize a portable, eas-

ily reproducible and programmable interface with a

highly selective ﬁlter and low power consumption.

The implemented interface is able to record sponta-

neous and/or evoked activity of neurons from eight

electric contacts of multi-site “thin-ﬁlm Longitudinal

Intra-Fascicular Electrode” (tfLIFE) (Yoshida et al.,

2006) and to convert them in digital format and trans-

mit it through the USB connection to the PC to be

processed.

2 SYSTEM IMPLEMENTATION

2.1 Architecture

Fig. 1 gives a functional overview of the neural-

electronic interface developed. It consists of three

main parts: a neural signals recording circuitry, a

stimulus generation circuit and a digital system con-

troller.

Recording Unit: The main task of the recording part is

to capture the neural signal from the implanted tfLIFE

and to bandpass ﬁlter it in order to remove unwanted

components from the neural spectrum. Once that the

neural signal has been cleaned, it can be digitally con-

verted before being transmitted to the digital control

211

Angius G., Carboni C., Loi D. and Barbaro M. (2010).

AN ELECTRONIC INTERFACE FOR NEURAL ACTIVITY RECORDING AND STIMULATION.

In Proceedings of the Third International Conference on Biomedical Electronics and Devices, pages 211-214

DOI: 10.5220/0002749702110214

 SciTePress

BPF

Amplifier

ADC

Digital System

Controller

for Data and Powering

Stimulation Circuitry

Recording

Circuitry

Electrodes

V/I

converter

Figure 1: Block diagram of the recording and stimulation

system.

unit. A programmable ampliﬁcation stage is required

in order to exploit the full scale range of the ADC.

The architecture of the recording unit provides a fully

differential path for the signal so as to maximize the

common mode rejection and reduce the effect of in-

terferences.

Stimulation Unit: In the stimulation circuitry nerve

afferent activities towards the electrode are generated

by the digital system controller and converted ﬁrst

into an analog voltage through the DAC then, into

a stimulation current signal by means of a V/I con-

verter. The function electrical stimulation (FES) sig-

nal can be software programmable in amplitude, fre-

quency and shape.

Digital Unit: The digital system controller has two

main tasks. First, it provides power from a recharge-

able battery to ensure the isolation of the patient from

the electric grid (both for safety reasons and to re-

duce the noise injected by the grid). Main task of the

module, however, is managing the conﬁguration of

the whole system with the generation of timing sig-

nals (for multiplexers, ADC, DAC), programmable

gain and stimulation patterns. For a high-speed real

time data transmission and control, two different units

were integrated in the system: a Universal Serial Bus

(USB) controller and a wireless IEEE 802.11 module.

2.2 Working Modes

The implemented system works on three independent

modes: Recording, Stimulation and Testing mode.

This high degree of reconﬁgurability is guaranteed

thanks to the presence of a network of switches driven

by three digital signals. In Recording mode, the neu-

ral signal is captured from the tfLIFE electrode and is

sent to the recording circuitry. The signal is ﬁltered,

ampliﬁed and digitally converted before being trans-

mitted to the digital system controller through SPI

communication. When the system is in Stimulation

mode, different digital patterns are generated by sys-

tem controller and converted into an analog voltage

by a DAC.

USB and Wi

Digital Signal Controller

DAC

Attenuation Circuitry

Band-

Pass Filter

Programmable Amplifier

ADC

Stimulation Circuitry

Analog Multiplexers

tf-

LIFE electrodes

USB and Wi

-Fi Controllers

Digital Signal Controller

DAC

Attenuation Circuitry

Pass Filter

Programmable Amplifier

ADC

Stimulation Circuitry

Analog Multiplexers

LIFE electrodes

Figure 2: Photograph of the implemented PCB system.

When the system is reconﬁgured for testing purposes,

the digital controller generates a test pattern that emu-

lates neural inputs and sends it to the DAC. Since the

converter output voltage swings from 0V to 3V , with

a resolution of 45.7µV , in order to manage and gen-

erate neural signals in the range of few microvolts, a

programmable attenuation circuit has been introduced

in the system.

3 CIRCUIT DESCRIPTION

Commercially available devices were adopted to build

up the interface and to validate the function of the cir-

cuits design. The main beneﬁts of this solution are

highly reproducible, simple portable and low cost.

The fabricated prototype is shown in Fig. 2. Spe-

cial attention has been paid on how to isolate the

analog module from the interferences (especially on

supplies) generated by the digital unit. For this rea-

son the system has been divided into analog and dig-

ital parts and implemented in two separate, sandwich

stackable, printed circuit boards (PCB), sizing respec-

tively 7 cm x 6.1 cm and 7.5 cm x 5.8 cm. The cho-

sen solution could be suitable for a future fully im-

plantable version of the interface. The whole system

is supplied by a rechargeable battery (3.7V) to en-

sure the isolation of the patient from the electric grid.

Lower supply voltages (3V and 1.5V) are provided by

two linear voltage regulators (Maxim, MAX1589 and

MAX1792).

3.1 Neural Recording Circuit

Since neural recordings from tfLIFEs appear like

spikes with peak-to-peak amplitudes of about 50µV

and frequency band in range from 900Hz to 2kHz, it

is mandatory to eliminate DC components (50Hz) as

well as EMG and biological unwanted components.

Fortunately, these interferences are band-limited be-

low 300Hz and can be separated from the neural sig-

nal through an appropriate band-pass ﬁltering inside

the frequency band of interest. For this reason, a se-

BIODEVICES 2010 - International Conference on Biomedical Electronics and Devices

212

lective ﬁlter was integrated in the registration unit. It

is made up of a 4th order high-pass ﬁlter, cascaded

with a 4th order low-pass ﬁlter. The basic ﬁlter cell

has been implemented with Multiple Feedback topol-

ogy, using a very low noise density (3nV /

√

Hz) and

fully differential input/output ampliﬁer (Linear Tech-

nology, LT1994). The ﬁlter design speciﬁcation re-

quired a gain of 32dB and a frequency band between

900Hz and 2.3kHz. The RC ﬁlter network was sized

to reduce the input-referred noise (IRN) to 250nV in

the band of interest. Once that the signal has been

ﬁltered, it can be further ampliﬁed through a pro-

grammable gain ampliﬁer before being digitized. In

Fig.3(a) the chosen ampliﬁcation circuitry is shown.

A digitally controlled potentiometer (R7) (Intersil,

ISL90727), in pair with a resistor of 100Ω (R6), al-

lows to obtain a gain factor between 1 and 100, for

a total of 127 different values. Note that, using R6

as potentiometer and R7 with a ﬁxed value of 100Ω,

it has been possible to use the same circuitry to ob-

tain the programmable attenuation circuit of Fig.3(b)

useful during the test operating mode. Finally, the

recording system includes an analog-digital converter

(Analog Devices, AD7687) with a resolution of 16-

bit and a power consumption of 1.3mW , that by SPI

communication provides to the digital control unit the

processed signal.

ISL90727

vdd

vcm

LT1994

outm

outp

ISL90727

(a) Ampliﬁcation cir-

cuitry

vdd

vcm

LT1994

outm

outp

ISL90727

(b) Attenuation cir-

cuitry

Figure 3: Circuitry used for ampliﬁcation and attenuation.

3.2 Neural Stimulation Circuit

Key role for the application is played by the neu-

ral stimulation circuit. The stimulation is done with

current patterns that have programmable amplitude,

frequency and shape. Fig. 4 shows the architec-

ture of the stimulation unit. The circuit was imple-

mented using a 16-bit D/A converter (Linear Tech-

nology, LTC2641) followed by a V/I converter (Texas

Instruments, OPA343). The basic idea is to con-

vert a programmable voltage, generated by the DAC,

into a stimulation current that will ﬂow through the

electrodes. Note that resistor Relectr represents the

impedance between the measurement (L1 −L4, R1 −

R4) and reference (L0 −R0) electrode.

DAC

Rstim

tfLIFE

vdd

OPA343

LTC2641

Relectr

L0,R0

Li,Ri

Figure 4: Neural stimulation circuitry.

3.3 Digital System Controller

Two high-performance Microchip Microcontrollers

have been used for the bi-directional communication

PC ⇐⇒ AnalogBoard: the dsPIC33Fj256GP506 and

the PIC18LF4550. The ﬁrst one allows to manage

the ADC and DAC modules by means of a SPI bus

and to choose the value of the digitally potentiometers

through an I

C bus. While the PIC18LF4550 is used

as USB controller in the communication PC −dsPIC,

providing a communication speed of 64KB/s per re-

port, according to the speciﬁc class of HID (Human

Interface Devices).

4 EXPERIMENTAL RESULTS

In order to characterize the ﬁlter, sinusoidal signals at

different frequencies have been generated by the DAC

and applied to the ﬁltering circuitry for testing.

−20

−10

Frequency (Hz)

Magnitude (dB)

Simulated BPF Frequency Response

Frequency (Hz)

Magnitude (dB)

Measured BPF Frequency Response

Figure 5: Plot of theoretical and measured frequency re-

sponse of the band-pass ﬁlter.

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5

−0.2

−0.1

0.1

0.2

Time (ms)

Amplitude (V)

(a) Signal at 400 Hz

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5

−0.2

−0.1

0.1

0.2

Time (ms)

Amplitude (V)

(b) Signal at 1.3 kHz

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5

−0.2

−0.1

0.1

0.2

Time (ms)

Amplitude (V)

Figure 6: Band pass ﬁlter output signal.

The frequency response of bandpass ﬁlter is shown

in Fig. 5. The graphic above shows the simulations

AN ELECTRONIC INTERFACE FOR NEURAL ACTIVITY RECORDING AND STIMULATION

213

results, while the lower panel shows the frequency re-

sponse obtained by the ﬁlter test. As shown in the

ﬁrst panel, the designed ﬁlter has a gain equal to 32dB

in bandwidth (900Hz −2.3kHz) while at 400Hz and

6kHz the attenuation of the signal is over 20dB. The

results of measurements show a lower gain (24dB)

and a band between 1.1kHz and 2kHz. The deviations

from the ideal behavior are due to manufacturing tol-

erances of the resistances and capacities used in the

feedback network of the ﬁlter. The selectivity of the

band-pass ﬁlter has been tested using as input for the

recording circuitry a sinusoidal signal at different fre-

quencies: 400Hz, 1.3kHz and 5kHz. The effect of the

band-pass ﬁltering is evident in Fig. 6(b), where the

sine at 1.3kHz is allowed to pass. The signals shown

in Fig. 6(a) and in Fig. 6(c), prove how out-band fre-

quencies are completely ﬁltered.

1520 1522 1524 1526 1528 1530

1.4

1.6

1.8

Time (s)

Amplitude (V)

(a) Input Signal

1520 1522 1524 1526 1528 1530

−0.8

−0.4

0.4

0.8

Time (s)

Amplitude (V)

(b) Matlab Filtered Signal

1520 1522 1524 1526 1528 1530

−0.8

−0.4

0.4

0.8

Time (s)

Amplitude (V)

Figure 7: Neural signal processing: ﬁltering, ampliﬁcation

and digital conversion.

Finally, the whole recording system has been tested in

its functionalities using stimuli supplied by the Scuola

Superiore di on the basis of recordings made in clini-

cal trials with rabbits. The input pattern represented in

Fig. 7(a) is the result of ten recording seconds during

which the rabbit was subjected to vibrations at 50Hz

and 100Hz in cutaneous afferents.

In a ﬁrst phase of simulation, the available input

pattern of Fig. 7(a) has been processed with Matlab

using an ideal band-pass ﬁlter. In Fig. 7(b) the ideal

ﬁltered signal obtained is shown. The heavy inﬂu-

ence of low-frequencies noise has been deleted and

is clear the presence of the useful signal components

that in the input pattern were completely masked. The

ﬁltered signal obtained with the implemented neural

recording circuitry, is represented in Fig. 7(c). The

slow components of the input signal coincide with

output peaks characterized by higher frequencies.

5 CONCLUSIONS

We have developed and tested a PCB system for neu-

ral activity recording and stimulation. Given the na-

ture of the signals to be acquired, special design tech-

niques for low noise and low power consumption have

been adopted. The ﬁrst experimental results prove

that the system works correctly and stably, with the

possibility to acquire and process neural signal in mi-

crovolts order. The recording circuitry allows to ﬁlter

the neural signals in the band of 1kHz −2.2kHz, pro-

viding a programmable gain that covers values from

24dB to 44dB. The resulting signal can be digi-

tally converted and sent to PC for further processing

through USB or Wi-Fi transmission. The acquisition

system has been tested using pre-recorded neural pat-

terns extracted by rabbit with tfLIFE electrodes. The

system has been studied in order to generate also elec-

trical stimuli with controlled current amplitude, du-

ration and shape. This effort is part of an on-going

research program which aims to develop smart im-

plantable devices dedicated to neural activity record-

ing and stimulation.

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