A FRONT-END STAGE FOR NEURAL SIGNAL RECORDING

BASED ON A SIGMA-DELTA MODULATOR

Caterina Carboni, Daniela Loi and Massimo Barbaro

Dept. of Electrical and Electronic Engineering, University of Cagliari, Cagliari, Italy

Keywords:

Neural signal recording, Low noise ampliﬁer.

Abstract:

A new device for peripheral neural signals recording is presented. The designed system is composed by an

analog and a digital part. The analog part, to be integrated on an implantable CMOS chip, is kept as simple

as possible and hosts a low noise ﬁrst order pre-ampliﬁer/pre-ﬁltering stage that provides a 46dB gain in the

bandwidth 800Hz − 7.2kHz and a 16-bit 3rd order sigma delta modulator. A highly selective band-pass ﬁlter

is implemented into the digital domain, incorporated in the decimator block of the sigma-delta converter; in

this way it is possible to reduce total area (which is 0.4mm

for a single input channel) and power consumption

(250µW , single channel) in the integrated, implantable module. Simulation results prove the capability of the

proposed system to record signals whose magnitude is in the order of tens of microvolts thanks to the low

Input Referred Noise (IRN) of 2.4µV

rms

of the input stage.

1 INTRODUCTION

In the last decades, research on biomedical engineer-

ing has been widely propelled by many groups with

the aim to ﬁnd a solution for a number of diseases

and to grant a better quality of life to patients. One of

the topics that has been subject to a great attention is

the interface between the human nervous system and

the machines (BMI). Such studies have a wide range

of potential applications: from diseases affecting the

nervous systems (like Parkinson, epilepsy, multiple

sclerosis) to treatment of permanent damages of the

spinal cord and, ﬁnally, prosthetics (Pereira et al.,

2007; Liu et al., 2000; Von Arx and Najaﬁ, 1999).

This work is included in the latter ﬁeld: our aim, in

fact, is to develop a system capable to record the sig-

nals captured by an electrode inserted in the periph-

eral nervous system of an amputee patient and exploit

them to drive a robotic limb. With this approach, there

are several advantages with respect to the more tra-

ditional Electromyographic (EMG) prostheses. First,

the limb will be controlled using the thought, thus al-

lowing the patient to feel the prosthetic arm as a nat-

ural extension of his/her body. Secondly, the inser-

tion of the electrode inside the nerves makes it possi-

ble to restore the sensory feedback, providing current

pulses whose shape, frequency and amplitude can be

programmed according to stimuli coming by temper-

ature and pressure sensors collocated on the robotic

limb (Micera et al., 2010; Dhillon and Horch, 2005).

The main problem, in this kind of application, is the

weak amplitude of the extracellular nervous signals

that can span in a range from few microvolts to 500

microvolts (Harrison and Charles, 2003; Yoshida and

Stein, 1999; Loi et al., 2011). Moreover, such sig-

nals are drowned in a noisy environment mainly due

to electromyographic (EMG) interferences from the

muscle fascicles surrounding the nerves; EMG sig-

nals are in the order of magnitude of millivolts, thus

several times the amplitude of the neural signals. Fur-

thermore, power spectral densities (PSD) of the in-

terferences and of the useful signal are very close and

partially overlap (Rieger et al., 2003; Harrison, 2007).

In this context, it is mandatory to properly amplify

the weak useful signal and to ﬁlter out the huge noise

component. Given the partial overlap of the PSDs, a

highly selective ﬁlter is needed to reject most of the

interferences. The implementation of such a circuit in

the analog domain is possible and it is a widely used

approach (Guo et al., 2004; Lee and Lee, 2005; Lim-

nuson et al., 2009), but it implies a number of prob-

lems in the design: highly selective ﬁlters are prone

to instability and cascading many ampliﬁers is power

consuming. Such problems make the implementation

of an integrated device for chronicle implants difﬁ-

cult. In our approach, we propose to adopt a sigma-

delta architecture which is capable to shift most of the

complexity of the realization either of the highly se-

207

Carboni C., Loi D. and Barbaro M..

A FRONT-END STAGE FOR NEURAL SIGNAL RECORDING BASED ON A SIGMA-DELTA MODULATOR.

DOI: 10.5220/0003782802070212

In Proceedings of the International Conference on Biomedical Electronics and Devices (BIODEVICES-2012), pages 207-212

ISBN: 978-989-8425-91-1

 2012 SCITEPRESS (Science and Technology Publications, Lda.)

lective ﬁlter and of the analog-to-digital conversion in

the digital domain thus keeping only a small analog

circuitry in the implantable chip. In this way a more

robust and noise immune system can be designed with

a lower power consumption. The device is therefore

made up of two modules: an implantable integrated

circuit and an external board. The microchip hosts

only a ﬁrst-order pre-ampliﬁer/pre-ﬁltering stage and

the sigma delta modulator (each of which for every

input channel) while the highly selective ﬁlter is im-

plemented altogether with the decimator on the exter-

nal digital platform. In this paper we present the de-

sign and the simulation results concerning the analog

part of the recording path. In section 2 we present the

architecture of the whole system, while in sections 3

and 4 we report the design choices respectively for

the pre-ampliﬁer/pre-ﬁltering block and for the delta-

sigma modulator. Simulation results are provided in

section 5.1 and conclusions are drawn in section 6.

2 SYSTEM ARCHITECTURE

The block diagram in Fig. 1 shows the system archi-

tecture which is composed by two main blocks: the

analog front/end and the digital processing unit. The

analog module was designed in a 0.35µm CMOS pro-

cess from AMS (Austriamicrosystems) with double-

poly capacitors, 3.3V power supply and 4 metal lay-

ers. Area and power constraints are particularly com-

pelling since the chip must be integrated with the elec-

trode and implanted in the patient stump. The front-

end module works in a bidirectional way, it imple-

ments basic signal conditioning on the incoming neu-

ral signal (in recording mode) and provides current

pulses on the feedback path (in stimulation mode).

The signal is, thus, ﬁltered and ampliﬁed by the Pre-

Filtering/Pre-ampliﬁer block as close as possible to

the recording site. In this way, the noise due to long

cables and connection paths can be avoided. The con-

ditioned signal is then converted into a 1-bit digital

stream by the delta-sigma modulator and sent to the

digital module for decimation and further processing.

One of such signal’s streams is needed for each input

channel if the device is connected to a multichannel

electrode. The digital module is hosted on an exter-

nal board; it implements the decimation block of the

sigma-delta converter altogether with the highly se-

lective bandpass ﬁlter. The digital module is also re-

sponsible of the generation of digital programmable

stimulation waveforms and of management of the

communication between the artiﬁcial limb and the

electrodes. The digital unit will be implemented on

programmable logic (FPGA), hosted on the robotic

limb. A further advantage of such approach, is the

possibility of changing the selectivity of the ﬁlter on

the ﬂy by adjusting the digital parameters.

Pre

ampli

Pre-filtering

Implantable CMOS circuit

Electrodes

Current DAC for

stimulation

ΣΔ

modulator

ΣΔ

decimator

Digital

patterns

generation

Digital System

Controller on

FPGA

Figure 1: Block diagram of the recording and stimulation

system.

3 PRE-AMPLIFIER AND

PRE-FILTERING

Fig. 2 shows the pre-ampliﬁer/pre-ﬁltering block that

was implemented with a ﬁrst order switched capaci-

tor (SC) ﬁlter. SC-ﬁlters have, in fact, the great ad-

vantage, over their continuous-time counterpart, to

achieve a better precision in the cut-off frequency im-

plementation. The Bandpass Filter (BPF) was real-

ized cascading a Highpass ﬁlter (HPF) with a Low-

pass Filter (LPF); the HPF is used as ﬁrst stage in or-

der to start rejecting the EMG interferences as close

as possible to the electrode, before any ampliﬁca-

tion. The ﬁlter speciﬁcations required a bandwidth

between 800Hz and 8kHz and a gain of 200V /V . This

gain value has been determined to maximize the am-

pliﬁcation at neural frequencies avoiding the risk of

ampliﬁer saturation. In Table 1 the values used for

the ﬁlter capacitance are summarized.

HPF

Figure 2: Band-Pass ﬁlter.

The switches were realized using transmission

gates at minimum size in order to reduce area. A

BIODEVICES 2012 - International Conference on Biomedical Electronics and Devices

208

Table 1: Filter Capacitance values.

C Value [pF]

1hp1,2

2hp1,2

3hp1,2

1.7

C Value [pF]

1l p1,2

2l p1,2

1.6

3l p1,2

simple cascode symmetrical OTA (Fig. 3) was cho-

sen for the operational ampliﬁer implementation and

it was carefully dimensioned in order to minimize the

Input Referred Noise (IRN). Flicker noise is one of

the major concerns, given the low frequencies of the

neural signal, thus a p-type differential pair with large

area was adopted to minimize this contribute (Razavi,

2001). Thermal noise was addressed using a large g

for the differential pair and reducing the g

of the out-

put branch transistor (Harrison and Charles, 2003) ac-

cording to the formula:

n,th

16kT

(

1 +

m12

)

where k is the Boltzmann constant, T the absolute

temperature and g

the transconductance of transis-

tor x. In Table 2 all sizes are reported, all transistors

were biased with a 4µA current. A passive SC com-

mon mode feedback block was used for power saving.

M3 M6

M4M5

M12

M10

M11

M13

VddVdd

Vn Vp

Vbiasp

Vcasp

VcasnVcasn

Vcasp

Vctrl

VoutpVoutn

Figure 3: Symmetrical OTA.

Table 2: OTA dimensions.

[

µm

]

1,2

800

3,4

5,6

[

µm

]

7,8

9,10

11,12

4 SIGMA-DELTA MODULATOR

Once that the useful signal has been properly am-

pliﬁed it is possible to convert it in the digital do-

main. With this purpose, a third order switched ca-

pacitor sigma-delta modulator in a Cascade of Inte-

grators with FeedBack (CIFB) conﬁguration was de-

signed. The weakness of the neural signals required

to adopt a 16-bit converter; with a reference voltage

of V

re f

= 2V

, a LSB = 0.15µV can be achieved, al-

lowing the detection of microvolt level signals. This

result was achieved using an OSR = 128 with a sam-

pling frequency f

= 2.048MHz. Due to long simu-

lation time, preliminary tests were performed using a

behavioral model in Simulink environment and, only

once that the speciﬁcations were satisﬁed, the circuit

was implemented at transistor level.

4.1 Behavioural Simulink Model

A Simulink model has great advantages in terms of

simulation time saving moreover, it allows the intro-

duction of blocks modelling noise sources and opamp

non-idealities thus granting an excellent reliability of

the system and a good agreement with transistor level

simulations. Fig. 4 shows the diagram block used for

this purpose.

inputs

noise sources

3° order ΣΔ modulator

Figure 4: Sigma-delta modulator: behavioural Simulink

model.

The model is a modiﬁcation of what presented

in (Malcovati et al., 2003) and (Zare-Hoseini et al.,

2005) which take into consideration saturation, slew

rate, ﬁnite gain and bandwidth limitations. KT /C

noise, thermal noise of the ampliﬁer, switches non-

idealities and clock jitter effects have also been in-

cluded. With respect to the original model our system

was transformed in a fully differential architecture in

order to make it more similar to the actual transis-

tor implementation (shown in the following subsec-

tion). The coefﬁcients (summarized in in Table 3)

were chosen using the Schreier Toolbox (Schreier and

Gabor C., 2001) with a 18-bits target resolution (we

added 2 bits for noise margin).

The mismatch effects were also taken into con-

sideration since the coefﬁcients have been generated

A FRONT-END STAGE FOR NEURAL SIGNAL RECORDING BASED ON A SIGMA-DELTA MODULATOR

209

Table 3: Sigma delta modulator: coefﬁcients.

Coefﬁcient Value

0.05

0.3

0.8

Coefﬁcient Value

0.05

as capacitor ratios, whose values have been extracted

randomly from a normal distribution within a 6σ

range around the nominal value.

4.2 Transistor Level Design

The block diagram described in the previous section

was mapped into a transistor level design using a

SC circuit. The main components are the discrete

time integrators shown in Fig. 5 (ﬁrst stage) and

Fig. 6 (second and third stages). It can be observed

that all the feedback paths have been realized driv-

ing a switch with the quantizer output and connecting

the corresponding capacitor to the reference voltages

re f −

= V

/2 − 1 and V

re f +

= V

/2 + 1. Since the

a and b coefﬁcients are equals, in the ﬁrst stage the

same capacitor was shared for the two paths while

in the second and third stage two different capacitors

were used in order to implement coefﬁcients a and c.

Cab

InN

InP

fi1

fi2

nfi1

nfi2

fi2

fi1

fi2

nfi1

nfi2

fi1

nfi2

nfi1

OutP

OutN

fi2 nfi2

nbit bit bit nbit

Vref-

Vref+

bitbit nbitnbit

fi2 nfi2fi2nfi2

Vref

Figure 5: Sigma-delta modulator: ﬁrst integrator schematic.

In Table 4 all the values used for the capacitors

are reported, each coefﬁcient can be obtained by ca-

pacitance ratios using the relationship x = C

. A

simple symmetrical OTA was used to realize the oper-

ational ampliﬁer, it has been sized in order to meet the

speciﬁcations determined with the behavioral simula-

tion (in terms of DC gain, GBW, dynamic range and

slew rate). The single bit quantizer has been designed

with a track and latch circuit.

Figure 6: Sigma-delta modulator: second and third integra-

tor schematic.

Table 4: Sigma-Delta modulator: capacitance values.

C Value [pF]

ab,1st

0.2

f ,1st

a,2st

1.2

c,2st

C Value [pF]

f ,2st

a,3st

3.2

c,3st

f ,3st

Figure 7: Bandpass ﬁlter: Frequency response.

5 SIMULATION RESULTS

5.1 Pre-ampliﬁer/Pre-ﬁlter Results

The frequency response of the operational ampliﬁer

of Fig. 3 shows a DC-gain of 108dB with a GBW =

1MHz, the 62

phase margin ensures a good com-

promise between ringing avoidance and settling time.

The integrated input referred noise in the bandwidth

800Hz − 8kHz is 2.1µV

rms

allowing to detect signals

in the order of magnitude of tens of microvolts. The

bandpass ﬁlter provides a gain of 46dB within the 3dB

bandwidth 800Hz − 7.2kHz (Fig. 7) and the simu-

lated noise in this bandwidth is 2.4µV

rms

. The gain

BIODEVICES 2012 - International Conference on Biomedical Electronics and Devices

210

Figure 8: Sigma-Delta modulator PSD: behavioral sim-

ulation (red curve) and transistor level simulation (black

curve).

value was chosen to avoid the risk of saturation due

to EMG interferences. We considered a worst condi-

tion of a 10mV interference at 300Hz. In this case, the

gain at 300Hz is 65V /V thus the residual EMG signal

at the output of the ﬁlter will be 650mV

, far away

to rich the saturation limit of 2V

of the sigma-delta

modulator. This residual out of band huge interfer-

ence will be completely rejected in the digital domain

by the high-selective band pass decimator.

5.2 Sigma-delta Modulator Results

The power spectral density (PSD) of the sigma-delta

modulator, is shown in Fig. 8: the red curve rep-

resents the results obtained by Simulink simulations

while the black one is the PSD extracted by the tran-

sistor level simulation performed with Cadence. As

it can be observed, the two curves are very simi-

lar each other, conﬁrming the good agreement be-

tween the two models. The ﬁnal resolution achieved

is 16.12bit corresponding to a SNR = 98.8dB with

an OSR = 128. The system functionalities have been

tested using, as modulator input, pre-recorded neu-

ral patterns, on the basis of recordings made in clini-

cal trials with rabbits. The input pattern, represented

in Fig. 9(a), corresponds to 3.2 seconds of record-

ing during which the rabbit was subjected to vibra-

tions at 50Hz and 100Hz in cutaneous afferents. The

input signal is affected by EMG and ECG interfer-

ences with a very large amplitude (in the millivolts

range) and a spectrum (Fig. 9(b)) concentrated be-

low 300Hz. Such interferences completely mask the

underlying neural content. The signal has been pro-

cessed by the delta-sigma modulator and then deci-

mated and band-pass ﬁltered using a digital ﬁlter in

simulink environment. Fig. 9(c) displays the input-

referred output signal and Fig. 9(d) its power spec-

tral density: the low-frequency interferences are com-

pletely removed and the weak neural signal (in the mi-

crovolts range) is now visible, as well as its frequency

signature. In Table 5 the main characteristics of the

system are summarized.

6 CONCLUSIONS

A bio-electronic interface for peripheral neural signal

recording was designed. The ﬁrst stage is composed

by a ﬁrst order pre-ampliﬁer and pre-ﬁltering block

0 0.5 1 1.5 2 2.5 3

−2

−1

Time (s)

Amplitude (mV)

(a) Input Signal.

Frequency (Hz)

Amplitide (mV

)

(b) Input Spectrum.

0 0.5 1 1.5 2 2.5 3

−100

100

Time (s)

Amplitude (µV)

Frequency (Hz)

Amplitude (mV

)

(d) Output Spectrum.

Figure 9: Sigma Delta modulator: pre-recorded neural signal processing.

A FRONT-END STAGE FOR NEURAL SIGNAL RECORDING BASED ON A SIGMA-DELTA MODULATOR

211

Table 5: Main parameters summary. Data are referred to

each input channel.

parameter value

Gain 46dB

, f

800Hz, 7.2kHz

Power 250µW

Area 0.4mm

IRN 2.4µV

rms

resolution 16.12bit

that provides a gain of 200V /V in the bandwidth

range 800HZ − 7.2kHz. The signal is then converted

in the digital domain by a 16-bit sigma delta modula-

tor and transmitted to the digital part of the system for

decimation, highly selective band-pass ﬁltering and

further signal processing. The analog front-end of the

designed system (preﬁltering/preampliﬁer and sigma-

delta modulator) exhibits a total area of 0.4mm

with

a 240µV power consumption (for each input channel).

Simulation results show that the system is capable

to record a neural signal in the order of magnitude

of tens of microvolts thanks to its low IRN equal to

2.4µV

rms

ACKNOWLEDGEMENTS

The work described was supported by MIUR (Ital-

ian Ministry of Education, University and Research)

through project Openhand (PRIN 2008). The au-

thors wish to thank Laura Muggianu for helping with

the design and simulations and Silvestro Micera of

Scuola Superiore Sant’Anna di Pisa (SSSA) for neu-

ral traces providing.

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