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IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, VOL. 61, NO. 5, MAY 2014

Integrated Circuits and Electrode Interfaces for
Noninvasive Physiological Monitoring
Sohmyung Ha, Student Member, IEEE, Chul Kim, Student Member, IEEE, Yu M. Chi, Member, IEEE,
Abraham Akinin, Student Member, IEEE, Christoph Maier, Member, IEEE, Akinori Ueno, Member, IEEE,and Gert Cauwenberghs∗ , Fellow, IEEE

Abstract—This paper presents an overview of the fundamentals
and state of the-art in noninvasive physiological monitoring instrumentation with a focus on electrode and optrode interfaces to thebody, and micropower-integrated circuit design for unobtrusive
wearable applications. Since the electrode/optrode–body interface
is a performance limiting factor in noninvasive monitoring systems,
practical interface configurations are offered for biopotential acquisition, electrode–tissue impedance measurement, and opticalbiosignal sensing. A systematic approach to instrumentation amplifier (IA) design using CMOS transistors operating in weak inversion is shown to offer high energy and noise efficiency. Practicalmethodologies to obviate 1/f noise, counteract electrode offset
drift, improve common-mode rejection ratio, and obtain subhertz
high-pass cutoff are illustrated with a survey of the state-of-theart IAs. Furthermore, fundamental principles and state-of-the-art
technologies for electrode–tissue impedance measurement, photoplethysmography, functional near-infrared spectroscopy, andsignal coding and quantization are reviewed, with additional guidelines for overall power management including wireless transmission. Examples are presented of practical dry-contact and noncontact cardiac, respiratory, muscle and brain monitoring systems,
and their clinical applications.
Index Terms—Biological signal sensing, biomedical electronics, body–electrode interface, electrode contacts, electrode–tissue
impedance, functional near-infrared spectroscopy, health monitoring, instrumentation amplifier (IA), optrode interface, photoplethysmography, sensor interface.

I. INTRODUCTIONNONINVASIVE physiological monitoring technology has
advanced tremendously over the years making a substan-

Manuscript received December 5, 2013; revised February 11, 2014; accepted
February 16, 2014. Date of publication February 26, 2014; date of current version April 17, 2014. This work was supported in part by the National Science
Foundation M3C EFRI-1137279, in part by The Defense Advanced Research
Projects Agency, in part by the Texas Instruments, in part by the NEDO of
Japan. The work of S. Ha was suppported by a Fulbright fellowship. Asterisk
indicates corresponding author.
S. Ha, C. Kim, A. Akinin, and C. Maier are with the Department of Bioengineering and the Institute for Neural Computation, Jacobs
School of Engineering, University of California, San Diego, La Jolla, CA
92093 USA (e-mail: soha@ucsd.edu; chk079@ucsd.edu; aakinin@ucsd.edu;
christoph.maier@ieee.org).
Y. M. Chi is with Cognionics Inc., San Diego, CA 92121 USA (e-mail:mikechi2@gmail.com).
A. Ueno is with the Department of Electrical and Electronic Engineering, Tokyo Denki University, Adachi-ku, Tokyo 120-8551, Japan (e-mail:ueno@mail.dendai.ac.jp).
∗ G. Cauwenberghs is with the Department of Bioengineering and the Institute for Neural Computation, Jacobs School of Engineering, University ofCalifornia, San Diego, La Jolla, CA 92093 USA (e-mail: gert@ucsd.edu).
Color versions of one or more of the figures in this paper are available online
at http://ieeexplore.ieee.org.
Digital Object Identifier 10.1109/TBME.2014.2308552

tial impact on medical diagnostics and personal healthcare, fromthe early fundamental advances in noncontact and dry sensing
technology [1]–[3] to most recent advances extending the rangeof physiological sensing using imaging and electrical sensing
technology abundantly available in handheld devices and household appliances [4]–[7]. CMOS technologies and circuit techniques have facilitated the development and miniaturization of
innovative physiological sensing devices, improving the performance, power and monetary costs while ensuring the validity of
medical information through analog and digital signal processing methods. These IC developments have permitted reliablenoninvasive measurement of vital parameters and have spawned
a variety of new instruments for clinical treatment and diagnosis.
Innovations by semiconductor technologies enable ambulatory continuous-time monitoring of patients even at home. Thisubiquitous monitoring supported by modern IC technology canenable personalized healthcare and preemptive medicine, which
are emerging solutions to soaring healthcare costs induced by
the current demographical trend of increasing aging population.
The patient-supporting sensors and systems not only extend thecapability and accuracy of modern diagnostics, but also improve
the patient’s everyday life. In addition, miniaturized electronicsystems for biosignal sensing can be tailored to many nonclinical applications such as sports and entertainment.
Fig. 1(b) shows the main functional components of a generic
IC for noninvasive physiological monitoring, comprising analog front-end (AFE), analog signal processor (ASP), analogto-digital converter (ADC), digital signal processor (DSP), radio frequency (RF) communications, and power management.
This review surveys these components with a focus on the corefunctions of AFE, ASP, and ADC implemented in low-noise,
low-power custom integrated circuits, and tailored to the signal
conditions and range of the physiological variables of interest.
Foremost, a solid and thorough understanding of the electrode–body interface is of primary importance for accurate and reliablenoninvasive physiological sensing and signal acquisition. The
following section reviews fundamentals of electrode–body andoptrode–body interfaces for biopotential acquisition, impedance
measurement, and optics-based sensing.
II. NONINVASIVE ELECTRODE INTERFACES TO THE BODY
A. Electrode Interfaces for Biopotential Sensing
The coupling of biopotential signals from the body into thefront-end amplifier is accomplished through electrodes. At afundamental level, the electrode interfaces ionic currents in the0018-9294 © 2014 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.
See http://www.ieee.org/publications standards/publications/rights/index.html for more information.

HA et al.: INTEGRATED CIRCUITS AND ELECTRODE INTERFACES FOR NONINVASIVE PHYSIOLOGICAL MONITORING

Fig. 1. Noninvasive physiological monitoring. (a) Signal modalities of electrical and vascular activity internal to the body available for measurement on the
periphery. See Table I for glossary. (b) Integrated circuits (ICs) and electrode interfaces for signal acquisition, coding, and transmission. Example micrographs
are shown for (c) a biopotential front-end and acquisition IC interfacing throughbit-serial daisy chain with (d) a power and data telemetry IC [8].
TABLE INONINVASIVE PHYSIOLOGY AND BODY SIGNALS

body with electrical currents in the electronic instrumentation.
In practice, because the electrode comprises the first stage of thesignal chain, its properties can dominate the overall noise andperformance of the acquisition system making its design andselection crucially important.
Broadly speaking, there exist three classes of biopotential
electrodes in the literature: wet, dry, and noncontact as shownin Fig. 2 [9], [13]. All types of electrodes ideally measure theexact same biopotential signals and are largely differentiated by
the presence of a gel and the resulting contact impedance to thebody.
Wet electrodes are the most common type and considered
the “gold standard” for both clinical and research applications. A typical wet electrode consists of a silver–silver chloride(Ag/AgCl) metal that is surrounded by a wet or solid hydrogel,containing chloride. Other kinds of metals can be used (gold iscommon for EEG) if the dc stability of the Ag/AgCl electrode is
not necessary. The primary drawbacks with wet electrodes are
its longevity and comfort. Wet electrodes degrade as the moisture content evaporates limiting its useful lifetime to, at most, a1523

Fig. 2. Electrical coupling of the skin–electrode interface for various electrode
topologies, including (a) wet-contact gel-based Ag/AgCl [9], (b) dry-contact
flexible thru-hair sensor [10], (c) thin-film insulated capacitive metal plate [11],and (d) noncontact metal plate coupling through hair or clothing such as cotton [12]. (e) Measured noise spectrum of various electrode types, placed atclose proximity on forearm at rest, along with predicted (dotted lines) thermal
noise limits from measured skin–electrode coupling impedance data [9]. The
instrumentation noise floor of the shorted amplifier is also shown for reference.

few days. Many users also report skin irritation and discomfort
from the gels and adhesives that contact the skin.
Dry electrodes operate without the use of an explicit wet/gel
coupling media. The metal in the electrode directly contacts
the skin to couple biopotential signals. In practice, however,virtually all dry electrodes still rely on some degree of moisture which is gathered from the environment or emitted fromthe body (e.g., sweat). Compared with the wet electrodes, theperformance of a dry electrode usually increases over time as
more moisture permeates the skin–electrode interface resulting
in increased coupling. On bare skin, dry electrodes normallyexhibit higher contact impedances than wet electrodes by one
order of magnitude difference [14]. However, with modern highinput impedance amplifiers, this is rarely an issue. As with wetelectrodes, Ag/AgCl contact materials tend to show the best performance, especially in terms of drift noise, which is importantfor diagnostic ECG applications.
There are also dry-contact electrodes with capacitive coupling
between the electrode and the body instead of coupling via galvanic conduction. Capacitive contact electrodes utilize a thindielectric layer to form an insulated contact to the body. Compared to standard dry-contact electrodes, capacitive electrodes
offer a galvanically isolated, chemically inert surface, maximizing user safety and electrode longevity. However, the capacitive
interface precludes the measurement of true dc potentials andmay result in long settling times depending on the bias resistor and the amount of coupling to the body. In other respects,
the capacitive contact electrodes operate similar to dry-contact
electrodes.

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Fig. 3.

IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, VOL. 61, NO. 5, MAY 2014

Body–electrode interfaces for (a) EIT and (b)–(d) ETI measurements.

The final type of electrodes, noncontact, can be thought ofas a special case of dry electrodes. They operate not only without gel, but also through an insulation layer such as clothing,enabling signal acquisition without direct skin contact. As expected, the coupling impedance can be very high on the orderof tens of picofarad in parallel with hundreds of megaohm.
Obtaining acceptable signals requires the use of special, very
high input impedance active electrodes. Because there is no
direct skin contact, movement artifacts are a major, unsolved
issue especially for ambulatory use. Noncontact electrodes arealso highly sensitive to environmental conditions such as humidity and the exact insulating material. Noncontact electrodes
tend to work well on natural fabrics (e.g., cotton) under highhumidity where the fabric actually becomes slightly conductive,
offering a galvanic path to the skin. In contrast, it is difficult toobtain acceptable signals on high insulating synthetic fabrics(e.g., polyester) due to triboelectric artifacts.

B. Electrode Interfaces for Impedance Sensing
Controlled activation of current sources at electrode interfaces for biopotential sensing directly extends their use to sensing of electrical impedance. Two applications of impedance
sensing are of particular interest: electrical impedance tomography (EIT) for medical imaging by spatial mapping of tissue
impedance across body parts, and tissue–electrode impedance(ETI) checking for electrode contact quality monitoring.
EIT across a suitably sized array of electrodes placed over the
body surface offers a low-cost, low-profile, noninvasive medical imaging modality free of ionizing radiation [15], [16] thathas been applied to detection of breast cancer [17], imaging of
brain function [18], [19], and monitoring of lung function [20].
A basic four-electrode setup for one EIT impedance element
is illustrated in Fig. 3(a) [21]. High-impedance current sources
inject complementary ac currents through two electrodes, while
voltage is recorded between separate high-impedance senseelectrodes. The recorded voltage is hence independent of any
electrode impedance, and directly conveys tissue impedance between the body terminals interfacing with the sense electrodes.

A simple scheme for measurement of electrode–tissue
impedance using three electrodes as shown in Fig. 3(b) has been
implemented for the tracking of biopotential electrode quality
and identification of movement artifacts [22], [23]. The voltage between one electrode driven by a high-impedance current
source and another high input-impedance sensing electrode ismeasured to obtain only the electrode–tissue impedance connected to the ac current source [22]. Although activation of one
ac current source can be interchanged between the two electrodes in order to measure both impedances in sequence, morereliable measurement is obtained by simultaneous activation of
two equal magnitude ac current sources as shown in Fig. 3(c).
The sum of electrode impedances is obtained from measurementof the voltage difference during activation of currents with opposing polarities, while the difference of impedances is obtained
for same-polarity currents [24]–[26]. A simplified arrangementfor directly estimating individual electrode impedance is shownin Fig. 3(d). This arrangement takes advantage of the availabilityof a low-impedance body ground connection and the fact thatbody tissue impedance is negligible in comparison to tissue–
electrode impedance.
A standard driven right-leg (DRL) scheme with high-gain
negative feedback between separate ground sense electrode andthe drive ground electrode ensures that the body is at the system
ground potential regardless of the injected electrode current[27], [28].
C. Optrode Interfaces for Spectrophotometric Sensing
Spectrophotometric measurement of blood oxygen saturation has been widely adopted in clinical and outpatient settings as it simultaneously provides information about heart rate,
blood pressure variation, and respiratory function in a completely noninvasive modality at low power [30]. Light fromtwo different light-emitting diodes (LEDs), at red (660 nm)
and infrared (940 nm) wavelengths, is shined upon a body areawith good perfusion, and quantification of either the transmitted or reflected light is done to calculate absorbance throughthe tissue by a photodiode. In this function, pulse oximeters
are commonly presented as finger clips or rings and generally
use transmission mode measurement, while more recent applications such as patches, wristwatches, and smartphones require a reflection-based system [31], [32] as shown in Fig. 4(a).
Just like biopotential measurements, optical techniques can also
be applied to quantify brain activity. Functional near-infrared
spectroscopy (fNIRS) measures cerebral oxygenation, whichis related to activity, through the skull [29]. fNIRS produces
functional brain images from neighboring sources and detectors
placed on the head as shown in Fig. 4(b). Distance and configuration of sources and detectors placement determines spatial
resolution of the fNIRS. Recently, advances in integrated siliconavalanche photodiodes have dramatically improved the performance and wearability [33].
D. Problems and Challenges at the Interface
In addition to circuit noise from amplifier components, electrodes can be a significant noise contributor in the signal chain

HA et al.: INTEGRATED CIRCUITS AND ELECTRODE INTERFACES FOR NONINVASIVE PHYSIOLOGICAL MONITORING

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Fig. 5. Characteristics of EEG, ECG, and EMG, in relation to mains interference, electrode offset drift and 1/f noise [43], [44].

Fig. 4. Interface for (a) photoplethysmography and (b) functional nearinfrared spectroscopy (fNIRS). (c) Absorption factors of oxyhemoglobin, deoxyhemoglobin, and water with respect to the light wavelength [29].

[9], [34]. Unlike circuit noise, however, comprehensive models
for electrode noise do not exist, in part because the mechanisms
for electrode noise are not well understood. In general, electrodenoise is strongly correlated with the contact impedance but theactual level is significantly higher than the thermal noise fromthe resistive portion of the impedance.
The aggregate sum of the√ electrode noise sources can be quite
large, in the order of μV/ Hz at 1 Hz, even for wet electrodes.
This far exceeds the noise contribution of circuit components,
illustrating the importance of proper electrode selection. Due
to integrated current noise, both wet and dry electrodes have
sharp 1/f 2 spectra, which shows up as baseline drifts in thetime domain.
Noncontact electrodes can pick up additional noise from theinsulating material between the metal and the skin. As an example, acquiring signals through fabrics can be noisy due to theintrinsic high resistance of the fabric (>100 MΩ). This amounts
to the equivalent of inserting a large resistor in series with theamplifier input and can add significant noise in the signal bandwidth.
Outside of controlled laboratory conditions, the largest noise
sources will likely be electromagnetic interference (EMI) andmovement artifacts. The most common symptom of EMI is seen
as 50/60 Hz power line pickup. Triboelectric charging during
subject movement is also a large, albeit less understood, sourceof interference. As a subject moves, the potential between the
body and environment changes due to charge generation fromstriking the ground. This can couple into the system just likeEMI and is often mistaken for movement artifacts induced byelectrode–skin displacements.
EMI can be reduced or eliminated by a few simple techniques.
Common-mode interference is easily attenuated through the useof a DRL circuit [27], [28]. This technique is well understoodand operates by actively biasing the body potential toward afixed circuit reference potential through the use of negative feedback. This has the effect of reducing the magnitude of common

mode interference seen by the amplifiers. Differential pickup of
EMI is mitigated through the use of active electrodes with anamplifier placed in close proximity to the electrode [35]–[39].
Alternatively, the use of shielded lead wires is also highly effective at accomplishing the same goal, minimizing the total areaof high impedance traces that are susceptible to external electricfields [36].
Movement artifacts are a difficult challenge due to the lack ofquantified metrics and clear design methodology. Any physicaldisplacement between the electrode and the skin will necessarily
generate noise, sometimes many orders of magnitude larger than
the actual signal.
Movement artifacts can be eliminated by increasing the physical coupling pressure between the electrode and skin but may
conflict with the need for comfort and wearability. Reducing
movement artifacts highly relies on mechanical and industrial
design, and solutions are highly dependent on the specific end
application. In addition, electrode–tissue impedance measurement and signal processing techniques can be used to quantify
and suppress movement artifacts [23]–[26], [40]–[42].
III. AFE FOR NONINVASIVE SENSING
A. Physiological Requirements
The main design requirements of the AFE, ASP, and ADC
are driven by the characteristics of the physiological signals andthe body–electrode interface.
1) Biopotential Sensing: Biopotentials, such as EEG, ECG,
EMG, etc., are generated from volume conduction of currents
made by collections of electrogenic cells. EEG is the electricalpotential induced from collective activities of large number ofneurons in the brain. ECG results from action potentials of cardiac muscle cells, and EMG from contractions of skeletal muscle
cells. Various other biopotentials (EOG, ERG, EGG, etc.) also
result from collective effects of large numbers of electrogenic
cells or ionic distribution.
Almost all biopotentials, including EEG, ECG, and EMG,
of which characteristics are shown in Fig. 5, range over very
low frequency, typically less than 1 kHz. They are very low inamplitude ranging tens to hundreds microvolt when measured bya surface electrode. Since EEG and ECG range down to less than
1 Hz, recording of these signals faces challenges in electrode1526

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TABLE II
DESIGN FACTORS AND TRADEOFFS IN INTEGRATED ELECTRODE INTERFACES FOR NONINVASIVE PHYSIOLOGICAL MONITORING

offset voltage, which may reach up to 100 mV, varying slowly
over time. In addition, 1/f noise needs to be suppressed if theapplication calls for low noise at low frequencies (<1 Hz). Also,
common-mode interference from the mains and other irrelevant
biopotentials should be sufficiently rejected.
2) Electrode–Tissue Impedance Measurement: The measurement of electrode–tissue impedance (ETI) is implemented
by sensing a potential between electrodes while injecting analternating current. Therefore, most of the requirements forbiopotential sensing also apply to ETI measurement circuit.
However, in order to identify and suppress movement artifacts,
the electrode–tissue impedance and biopotential signal should
be sensed simultaneously [23], [24]. This requires chopper
stabilization schemes for the impedance measurement toseparate electrophysiological from resistive and reactive
impedance signals, each occupying different frequency bands(see Section III-H).
As additional advantage, chopper stabilization shields theelectrode from any dc currents, such as caused by imbalances
between source and sink currents and which lead to harmonic
distortion and saturated outputs [26]. The high dynamic range ofkiloohm range electrode–tissue impedances amidst ohm-range
body tissue impedances [45], could further be a potential sourceof saturation, mitigated by varying the amplitude of the ac current. Larger injected ac currents lower the input-referred noiseat the expense of power.
3) Optics-Based Sensing: Pulse oximeters and fNIRS systems have similar system requirements. The bandwidth of thehemodynamic and oxygenation signals ranges from approximately dc to 20 Hz. However, it is preferable to sample thesesignals at greater than 240 Hz, the Nyquist frequency of fluorescent light, to prevent aliasing of 60-Hz background interference [31]. Light attenuation is in the range of 10−4 –10−3 , withreflection-based systems showing greater attenuation than transmission systems [46]. The ac/dc contrast ratio can be as low as
0.25–1% of which accuracy must be within 2% [30]; thus contrast detection in front-end system must have a resolution ofaround 0.01% while being able to reject a large range of dc lightincidence due to the variability in application (patients, environment, movement artifact, etc.). Peak LED driving currents
for pulse oximetry are in the few tens of microampere range
and the duty cycle for the low-power pulsed system can be as
low as 1–3% [46], [47]. The light source to detector distance isapproximately 3 mm in reflection oximetry systems [32], while
in fNIRS this separation must be around 3 cm to be able to image the cerebral cortex noninvasively [48]. Additionally, fNIRS
systems require higher CMRR to reject interference [49].
B. Design Factors, Interrelations, and Tradeoffs
Several factors quantifying the metrics of performance andcost in the design, their interrelationships, and typical ranges
from the literature, are summarized in Table II. Several ofthese relationships, such as between noise, power, bandwidth,
gain, and dynamic range are generally well understood derivingfrom fundamental physical and information theoretic principles, e.g., power is typically linear in bandwidth but subject tonoise considerations. Other relationships, such as between input
impedance and movement artifact rejection, are specific to thephysiological signals and environmental factors at the electrodeinterface. The various intertwined relationships between thesefactors must be cooptimized in the design tradeoffs at the electrode, circuit, and architectural levels. A deep understandingof fundamental principles linking these factors and driving thetradeoffs is thus required. Specific tradeoffs and architectural design topologies that take advantage of properties of low-power
CMOS integrated circuits and systems are elaborated in thefollowing sections.
C. Subthreshold Operation of MOS Transistors
Counter to standard practices in analog CMOS circuit design,
the weak inversion (subthreshold) region of CMOS operation
has proven a favorable regime for low-power biomedical circuit design. In conventional design, particularly for high-speed
applications, weak inversion operation has been considered as
nonideality in a cut-off region and its current has been labeled
as leakage current. Recently, weak inversion has become increasingly important because its low-power and low-bandwidthcharacteristics are well suited for biomedical and other lowpower sensor applications, owing to superior transconductanceefficiency. Also, it does not suffer from many process-dependentproblems plaguing the above-threshold region in deep submicron technology, such as gain-limiting effects of velocity saturation in electron and hole mobility [50].
Transistor model equations in weak inversion are simpler,
more transparent, and scale over a wider range than in strong

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inversion. The electron energy of a transistor in weak inversion
is based entirely on the Fermi–Dirac distribution, independentof process technology. The drain current through the transistor
channel flows not by drift, but by diffusion, and changes exponentially with gate voltage [30]. Drain current iD S , transconductance gm , and unity-gain frequency ft in weak inversion are
as follows:


W
(1)
iD S = iD S 0 ev G S /(n V t ) 1 − e−v D S /V t
L
ID S
gm =
(2)
nVt
ft =

ID S
∝ ID S .
2πnVt (Cg s + Cg d + Cg b )(3)

Because the transconductance is linearly proportional to draincurrent, so is the unity-gain frequency. Thus, the tradeoff between current and bandwidth is very straightforward: the larger
the current, the wider the bandwidth.
Thermal noise in saturation and weak inversion is proportional to drain current as follows [51]:i2n ,th = 2qID S Δf(4)

where Δf is the signal bandwidth. The relative noise power(inverse of the signal-to-noise ratio) is inversely proportional todrain current:
i2n ,th
2
ID
S

=

2qΔf
.
ID S

(5)

Therefore, the signal-to-noise ratio is linearly proportional tobias current in weak inversion. For a majority of biomedical
applications with narrow signal bandwidth, the lower currentsof circuits in weak inversion still offer adequately large signalto-noise ratio at maximum energy efficiency.
Flicker noise, also known as 1/f noise or pink noise, is also
a significant noise source at low frequency. Random captures ofcarriers in traps near the Si/SiO2 interface and some other mechanisms are known to be a main source of 1/f noise [52], [53].
PMOS transistors are known to have less 1/f noise than NMOS
transistors, and therefore should be used in the input differential
pair of a front-end amplifier for low-noise low-frequency applications in biosensing. Enlarging the MOS device size also decreases 1/f noise inversely proportional to area. For low-noise
biomedical applications such as EEG acquisition, the chopper
stabilization technique is widely used to reduce 1/f noise further (see Section III-F). Other techniques such as auto-zeroing
and correlated double sampling can be used to reduce 1/f noise
as well [54].
D. Instrumentation Amplifier Design
One of the most challenging parts in the design of a wearable
physiological monitoring system is the implementation of instrumentation amplifiers (IAs), which acquire biosignals fromelectrodes and perform analog signal processing and conditioning. IAs are subject to almost all the challenging design specifications aforementioned in Section III-B.

Fig. 6. Generic architectures of (a) single-ended output [56] and (b) fully
differential instrumentation amplifiers. Measured (c) transfer function and (d)output noise power spectral density for different configuration settings of the
OTA bias current Ib ia s and pseudoresistance voltage bias V h p f [57].

A classic three-opamp IA is adequate for achieving large input
impedance, large CMRR, and sufficient gain. However, it consumes large power and area [55] since it uses three amplifiers.
For a micropower biopotential acquisition front-end, the configurations shown in Fig. 6(a) (similar to [56]) and (b) (similar
to [57]) are widely used. The ac-coupling input capacitors CC
block electrode offset voltages. Owing to favorable matching
performance of capacitors in integrated CMOS processes, thegain can be precisely controlled. A large resistor Rf , typically
implemented by a pseudoresistor or a switched-capacitor circuit(see Section III-E), establishes dc biasing of the voltage at the
input nodes of the amplifier and performs high-pass filtering
together with Cf . Mismatch in capacitor values results in the
degradation of the CMRR. A practical CMRR that this architecture can achieve is about 60 to 70 dB. In addition, CC dominates
the input impedance. Therefore, the value of CC needs to be set
by considering CMRR and the input impedance.
The passband gain is determined by the ratio of capacitors
CC to Cf . The high-pass cutoff frequency is set by the productof Rf and Cf . The low-pass cutoff frequency is approximately
gm Cf /2πCC CL , which can be controlled by the load capacitor
CL . The right-half-plane zero at gm /Cf can be canceled byinserting a 1/gm resistor in series with Cf . However, it canbe ignored in many cases because it is located at much higher
frequency than the frequency range of interest for biomedical
applications.
The main noise contributors of the single-stage IAs are theoperational transconductance amplifier (OTA) and the feedback
resistor Rf . In practical circuits, the noise from the OTA isdominant over the noise from Rf [58].
As a benchmark in the design of front-end IAs for low noiseand low supply current, the noise efficiency factor (NEF) is used

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Fig. 8. On-chip pseudoresistor implementations: (a) single MOS-bipolar
pseudoresistor [73], (b) pseudoresistor with two MOS-bipolar elements in series
for twice higher resistance and greater voltage range [56], [74], (c) symmetricalversion with outwardly connected gates, (d) symmetrical version with inwardlyconnected gates [39], [61], [75], (e) voltage-biased pseudoresistor for resistancetunability [57], [72], [76]–[78], (f) balanced tunable pseudoresistor with wider
linear range [65], (g) switched-capacitor implementation [62], (h) switchedcapacitor implementation with 10-times larger effective resistance than (g) [66].
(i) Measured resistance of a single MOS-bipolar pseudoresistor (a) as a functionof voltage [56].

Fig. 7. (a) NEF and (b) performance summary of state-of-the-art IAs fornoninvasive biomedical applications.

to compare the current-noise performance:
2Itot
NEF = Vrm s,in =πVt · 4kT · BW

(6)

where Vrm s is the total input-referred noise, Itot the total current
drain in the system, Vt the thermal voltage, and BW the −3dB bandwidth of the system [59]. The NEF corresponds tothe normalized supply current relative to that of a single BJTwith ideal current load for the same noise level, defining thetheoretical limit (NEF = 1). In practice, differential IAs with
input differential pairs incur twice the supply current for thesame transconductance, with NEF values greater than 2. The
state-of-the-art IAs typically have NEF of 2.5 to 10. As a pointof reference, measured NEF and performance of state-of-the-art
IAs are shown in Fig. 7(a) and (b), respectively, [22], [39], [56],[57], [60], [61]–[70].
Note that even though NEF is widely used to benchmark IAs,
NEF is nothing more but a tradeoff between only three performance metrics: bandwidth, noise, and current—excluding
many other critical performance factors such as CMRR, input
impedance, power consumption, input dynamic range, etc. Modified NEF metrics, one comparing power consumption insteadof current [71], and another including power consumption anddynamic range [72], have been proposed as more comprehensive
NEF alternatives.
E. Pseudoresistors for Subheartz High-Pass Cutoff
The high-pass cutoff frequency needs to be well below 1 Hz in
typical biomedical sensors, requiring ultrahigh resistance in the

teraohm range. Realizing subheartz time constants with on-chip
capacitors and poly resistors consumes an impractically large
area for integrated implementation.
The most prevalent solutions are combinations of 1–10 pF
capacitors with PMOS-based MOS-bipolar pseudoresistors asshown in Fig. 8(a)–(f) [56], [73]. The most basic topology
among these is a PMOS whose gate and body terminals areconnected as Fig. 8(a) [73]. This PMOS pseudoresistor combines a pn-junction in the forward direction (VA > VB ) with adiode-connected subthreshold PMOS in the reverse direction(VA < VB ). Owing to the source-bulk connection, the gateconnected drain terminal is leakage free and is ideally connected
to a leakage-sensitive side such as a floating input to an OTA.
The measured resistance of a single MOS-bipolar pseudoresistor is shown in Fig. 8(i) [56]. A configuration with the PMOS
gate connected to a bias voltage in Fig. 8(e) results in a controllable resistance by gate voltage [57], [72], [76]–[78]. However,
the resistance of the PMOS-based pseudoresistors in Fig. 8(a)–(e) drops drastically when the voltage across moves away fromzero, inducing signal-dependent distortion while limiting thevoltage dynamic range [65]. The pseudoresistor in Fig. 8(f) has
balanced resistance with wider linear range up to a few hundred
microvolt. An even wider linear range can be achieved by using
an auxiliary amplifier [79].
However, standard MOS-bipolar pseudoresistors suffer fromprocess, voltage, and temperature (PVT) variations in addition
to possible light and EMI sensitivities, leading to variations
in cutoff frequency. Switched capacitors can be used to implement on-chip PVT-insensitive high resistance as shown in
Fig. 8(g) [62]. In this topology, the switching frequency fs andthe capacitor in the middle determine the resistance precisely
as 1/fs C. The switched-capacitor resistor in Fig. 8(h) mitigates
manufacturability and interference issues by realizing a tenfold

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pass filter, and the desired input signal is ideally restored at theoutput Vout .
The residual offset is mainly caused by the nonidealitiesof the input chopper modulator. The mismatch of the clockfeedthrough and the charge injection in the input chopper generates switching transient spikes, which are demodulated at theoutput chopper into a residual output offset. In order to minimize
the offset, at first, careful design and layout need to be done. A
continuous [22], [62], [64], [66] or digital [39] dc servo loop
can reduce the residual offset, and mitigate the signal distortion
problem that is caused by the finite bandwidth of the amplifier.
Alternatively, filtering techniques [63], [86], [87] can be applied.
Output ripple is induced by the input offset of the amplifier, andcan saturate the output of the amplifier since offset is also amplified. The ripple can be reduced by a continuous ripple-reduction
loop [64] and a digital foreground calibration [39].

G. CMRR Enhancement TechniquesFig. 9. (a) Block diagram and (b) frequency-domain illustration of the chopper
technique for low-frequency noise and drift cancelation [54], [83]–[85]. (c)Input-referred noise spectrum with and without chopping [63].

resistance increase by charge sharing in the switched-capacitor
circuits [66].
F. Offset and 1/f Noise Cancellation Techniques
Autozeroing switched-capacitor techniques are often used tosuppress electrode voltage offset and 1/f noise of the amplifier [54]. However, opening of the reset switch on the sampling capacitor after autozeroing introduces significant Nyquist–
Johnson noise (kT /C noise) [80], [81] and random charge injection that contaminate the sampled signal. The kT /C noise of a
1-10 pF capacitance alone is about tens of microvolt. To resolve
this noise issue referred to the input of the AFE, signal folding
and digital-assisted signal stitching can be used, resulting in relieving the specification of voltage dynamic range [82]. Instead,almost all of the IAs utilize chopper stabilization techniques toobviate 1/f noise.
1) Chopper Stabilization Techniques: The chopper modulation technique is widespread and essential to mitigate 1/f noiseand other low-frequency noise, such as popcorn noise, voltage
offsets, and drifts, for EEG and other low-noise (< 1–2 μVrm s
input-referred noise) biopotential acquisition. The principles of
the chopper modulation technique for amplifiers, which havebeen extensively studied [54], [83]–[85], are illustrated in Fig. 9.
The low-frequency band-limited input signal Vin is modulated
in front of the amplifier by a square-wave chopping signal. The
resulting waveform Va for the signal is shifted to the chopping
frequency fch , and the aggressors do not fall within the signal band. After the amplification and demodulation with thesame chopping signal, the amplified input signal components
are shifted to dc baseband frequency at Vb while the aggressors
are moved to fch outside of the signal band. All the undesired
aggressors and the harmonics are filtered out through the low-

Common-mode interference is a difficult challenge forbiomedical signal sensing systems. The major source of theinterference comes from electric power lines, which are electrically coupled to the human body. A high CMRR is required in
the system to reject the common-mode interference in order toensure high signal quality.
Accurate component matching between differential signallines and between the channels is the most fundamental requirement to accomplish high CMRR. Good matching involves
techniques from careful layout to smart architectural design
choices.
1) DRL Technique: The DRL technique feeds the amplified
input common-mode voltage into the body through an additional
electrode, which has been placed on the right leg for ECG measurements. This negative feedback reduces the impedance inthe feedback loop, attenuating the common-mode interference
voltage at the sensor inputs [27], [28] by factor of the feedback
loop gain. Through the DRL negative feedback, the electrode
impedance and common-mode voltage are reduced by the factor of the DRL loop gain. Hence in order to obtain higher gain,
an open-loop DRL amplifier can be employed [88]. Digitallyassisted DRL circuits offer larger gain at the mains frequency forhigher rejection and lower gain elsewhere for stability [89]. In
dry-electrode applications, common-mode feedback to the negative inputs of individual amplifiers on the active electrodes also
increases CMRR, and ensures stability unaffected by electrode
impedance variations [39].
2) Input Impedance Boosting Techniques: The variation andmismatch of electrode impedances also degrade the CMRR, reduce signal amplitude, and make the system more susceptible tomovement artifacts. Thus, the input impedance of the biopotential sensor should be much higher than the electrode impedance
and the interface impedance between the body and the electrode.
A positive feedback can bootstrap the ac-coupled input capacitors to boost the input impedance [23], [39], [67], achieving input impedance in the order of gigaohm. In order to further boost
input impedance to the teraohm level, a unity-gain amplifier1530

IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, VOL. 61, NO. 5, MAY 2014

consumption of the sinusoidal ac current source, a simple pulsewave current source is implemented in [22], [24], and [25].
However, since a pulsed current contains large higher harmonics
that get folded into baseband, it leads to 23% measurement error.
To avoid large power consumption and harmonic distortions, a16-level quantized-sinusoidal current source is proposed in [45].
The sensed signals by the circuit structure in Fig. 10(a) are
the input of an adaptive filter with an LMS algorithm [24],[26]. Fig. 10(b) clearly shows that this method is able to reduce
the effect of movement artifacts with the estimated impedance
signals shown in Fig. 10(c) [23].
I. Optics-Based Sensing

Fig. 10. Electrode–tissue impedance measurement concurrent with biopotential recording. (a) Periodic current injection and synchronous detection ofresistive and reactive components of impedance along with the chopped biopotential signal. (b) Movement artifact reduction in an ECG signal through linear
correction with the estimated impedance signals [23].

with active shielding can be used to bootstrap capacitance of the
input transistor and all other parasitic capacitance [12].
H. Impedance Measurement
Movement artifacts can be suppressed by sensing electrode–tissue impedance and biopotentials simultaneously. In
Fig. 10(a), an ac current generation block with a chopper injects ac current into the sensing electrodes [23]–[25]. Due tothe electrode–tissue impedance, voltage signals including theimpedance information are superimposed on the original biopotentials. Note that the electrode–tissue impedance has resistiveand capacitive components resulting in real and imaginary components, respectively. All signals are directed to readout circuits.
To separate biopotential and impedance signals, only the IA for
biopotentials has a chopper at its input. For distinguishing realand imaginary parts in impedance signals, the output chopper
blocks use two phases in quadrature, fC H I and fC H Q . The IAwith fC H Q projects the voltage corresponding to the electrodereactance into the low-frequency band while other signal components are either canceled or projected to high frequencies andsubsequently rejected by low-pass filters, such that only signals
in low-frequency band are shown at the output [23]–[25].
A purely sinusoidal current generator for impedance probing
without a chopper block is reported in [90]. Due to large power

Pulse oximetry is accomplished by spectrophotometric measurement of the relative absorbances of arterial blood to red
and infrared light. The absorbance of the background tissue
and other interferences is rejected by sampling several times
throughout the period of a heartbeat, while the amount of bloodthat perfuses the probed area varies. The actual physical quantity observed is the ratio of the red over the IR contrasts, wherecontrast is defined as the quotient of the ac amplitude over thedc magnitude of the current generated at the photodiode foreach color. Once this ratio is computed, blood oxygen saturation can be computed with a simple formula [30]. The generalarchitecture of a pulse oximeter includes a probe, a photoreceptor, analog signal conditioning, and a digital processor andcontroller [46]. The probe consists of the two LEDs that generate the monochromatic signals, along with drivers, switchingand pulse modulation circuitry. The photoreceptor consists of aphotodiode to sense the incoming light and a transimpedance
amplifier (TIA) to amplify and convert the current driven by thesensor into a voltage. Consequently, analog low-pass filteringremoves artifacts from the switching and pulsing of the probe
light, which is generally much faster than the pulse signal ofinterest. Finally, the ratio between the red and IR signals (which
can come in parallel channels) can be computed in analog domain before digitization.
Integrated circuit advances in photoplethysmography havedramatically decreased the power consumption of these devices.
An ultralow power version of a whole system can consume 4.8
mW, or last 60 days on 4 AAA batteries [47]. This is possible
by reducing the duty cycle of the probe LEDs, by an innovative
logarithmic TIA with adaptive filtering and gain and by analog
ratio computation. Imaging brain activity with fNIRS systems
has a similar architecture with pulse oximetry. Advances include
integration of silicon avalanche photodiodes in the photoreceptor IC, along with a linear TIA for very low noise and high gain
detection [33]; and improvements in the spatial and temporal
resolution of multichannel systems by code-division-multipleaccess modulation of different emitters and detectors [48].
IV. SIGNAL CODING AND DIGITIZATION
Digitization of the recorded and processed analog physiological signals is required for further digital signal processing anddigital RF communication. The tight power and low noise constraints demand ultralow power ADCs at low-frequency range

HA et al.: INTEGRATED CIRCUITS AND ELECTRODE INTERFACES FOR NONINVASIVE PHYSIOLOGICAL MONITORING

1531(1–10 kHz) without sacrificing noise performance, while requiring no or very little static current drain and scalable power consumption with respect to sampling rate for multimodal recording
applications. Both successive-approximation ADC and oversampling ΔΣ ADC are superior architectures for achieving thespecifications with lowest power dissipation.
A. Successive-Approximation ADC
Successive-approximation register (SAR) ADC is the dominant architecture for low-power medium-resolution (8–12 bits)
biomedical applications due to its simple architecture involving few analog circuits and its low-power consumption at
low frequency without static power consumption [22], [23],[65], [70], [91]–[95]. Successive approximation of the sampled input performs a binary search over the input range using
a time-multiplexing switched-capacitor digital-to-analog converter (DAC) based on iterative partial one-bit quantization
results in the order from the most significant bit to the leastsignificant bit [96], [97].
Several techniques and architectures can be applied to minimize the power consumption of the SAR ADC. Using a mainand subbinary weighted DAC arrays with a series attenuation
capacitor can reduce the total size of the capacitor array; leading to reduced power consumption in the ADC driver and also
in the capacitor DAC [98]. Also, the folded capacitor DAC architecture with divided reference voltages reduces the size ofcapacitor DAC, resulting in further power saving [92]. Using
charge-recycling switching methods results in further powersavings in the switching of the capacitor DAC [99].
SAR ADCs are generally considered to be most energy
efficient for medium-precision low-sampling-rate digitization.
However, most micropower SAR ADCs operate at signal levels substantially (3–4 orders of magnitude) greater than typicalsignal level of physiological signals (see Fig. 5). They requiresignificant amplification before analog-to-digital conversion for
submicrovolt resolution. Furthermore, sampling at the Nyquist
frequency demands substantially more stringent antialiasing
analog filtering than required using oversampling techniques.
The cost of amplification and antialiasing filtering are often notaccounted for in ADC energy metrics. Most critically, samplingof biosignals at microvolt resolution is problematic due to kT /Csampling noise on capacitors which may amount to several tensof microvolt for typical picofarad-range capacitors in integrated
circuits.
B. ΔΣ Oversampling ADC
ΔΣ ADCs are an alternative solution with the following
strengths [57], [100]–[103]: 1) Resolution and sampling rate
can be dynamically reconfigured, with sampling rate proportional to power consumption, so they are adequate for multimodal biopotential sensor applications; 2) they require only few
and simple analog components; 3) they are suited for low-power
and low-voltage operation; 4) they easily achieve 12–16 bits or
higher resolution without complex circuit and layout techniques;5) continuous-time ΔΣ topologies are free of kT /C sampling
noise and less subject to aliasing and noise folding.

Fig. 11. Oversampling, aliasing-free biopotential acquisition system utilizing
Gm-C incremental ΔΣ ADC [57].

A Gm-C incremental ΔΣ ADC with widely configurable resolution and sampling rate is shown in Fig. 11 [57]. A transconductance (Gm ) cell converts the differential input voltage signal
to a current, approximately linear over the voltage range of typical biopotentials. The difference between this current and a feedback current is integrated and the resulting voltage is compared
for three-level quantization of the feedback current, implementing a continuous-time first-order ΔΣ modulator. A continuoustime oversampling ADC avoids the need for antialiasing filterand sample-and-hold circuits preceding the ADC. In addition,
direct digital control over the duty cycle in the feedback offers
precise digital gain programmability from 1 to 4096.
Another example incremental ΔΣ ADC for noninvasive
biopotential recording is given in [100]. It receives unbufferedbiopotential signals and performs amplification, signal conditioning, and digitization using only a single OTA. Other alternative ADC architectures include a hybrid architecture of SAR
and ΔΣ ADC [101], asynchronous level-crossing ADCs [104],[105], and a bioinspired ADC with the successive integrate-andfire operation [106].
The performance of ADCs can be measured and comparedby the Walden figure of merit (FOM), which energy
 quantifies
consumed per conversion step as Power/ 2ENOB fS . Fig. 12shows this FOM for all major ADCs reported from 1997 to 2013[107] including ADCs for noninvasive physiological monitoring
systems. [65], [72], [91], [92], [98], [101], [103], [104], [106],[108]–[110].
C. Application-Specific Signal Encoding
Digital signal processing (DSP) compressed encoding of thedigitized data prior to RF wireless transmission may lead to
substantial energy savings, particularly in high-dimensional or
high-bandwidth physiological sensing applications where theamount of useful information is significantly lower than thedata rate requirements of the raw signals. For example, in EEG
sensing for epileptic seizure detection [66], [70], 18 electrodes1532IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, VOL. 61, NO. 5, MAY 2014

Fig. 12. FOM versus ENOB of the state-of-the-art ADCs for biomedical
applications.

produce 200-Hz 12-bit data resulting in a total data rate of
43.2 kb/s. Employing digital processing for feature extraction
and seizure detection reduces this data bandwidth to 2 kb/s witha ten-fold reduction in power dissipation [66].
Equally importantly, energy-efficient analog preprocessing
can lead to significant reduction of data bandwidth and power inthe ADC in addition to the RF block. A low-power ADC and digital blocks can substitute a high-performance ADC and digital
signal processor. For example, typical EEG/ECoG-based brain–computer interface (BCI) applications do not need raw data, but
spectral characteristics of the recordings. Thus, extracting spectral power of the required frequency bands in the AFE canreduce power dissipation and/or complexity in the ADC, DSP,
and RF blocks [75]. Other applications of analog and mixedsignal preprocessing for low-power high-performance biosignal
processing include ECG physiological monitoring systems for
efficient QRS detection and coding [25], [95].
V. SYSTEM EXAMPLES
Examples of complete noninvasive physiological monitoring
systems covering a range of cardiac, respiratory, muscular, andbrain activity signals under a variety of environmental conditions, including underwater and noncontact sensing, are given
below. Application-specific design of the electrode interfaces
contribute to extend the applicable scope to ambulatory monitoring outside of the hospital.
A. Noncontact ECG Bed
One highly promising application of noncontact capacitiveelectrode interfaces is ECG monitoring embedded in beds
[111]–[114]. Integration of electrodes underneath a commercial
bed sheet (e.g., Fig. 13) enables noncontact electrode coupling,
through thin common nightwear, to a subject lying on the bed.
This implementation relieves the user not only of skin irritation but also of cumbersome electrode attachment, connecting
to ambient health monitoring and increased patient compliancefor every night use. Combined monitoring of ECG with breathing activity also extends the application area to neonatal supervision [115], [116] and sleep apnea screening. The breathing

Fig. 13. Example of noncontact electrode interface for monitoring ECG andbreathing on bed. (a) Fabric electrode placed on a bed and coupled indirectly tothe skin [112]. (b) Fabric bed electrodes placed on a bed for monitoring ECG
and breathing activity [111]. (c) Signal recording of narrow-band ECG (top)and simultaneously measured breathing activity (bottom) [115].

Fig. 14. Example of insulated electrode interface for measuring underwater
EMG. (a) Schematic of electrode configuration coated wholly with hydrophobic
insulator, (b) electrode placement, and (c) measured EMG signal from gastrocnemius muscle during plantar flexion with the electrode in (a) [117].

signal is obtained by capacitive sensing of displacement using
the same capacitive coupling electrode used for ECG sensing.
B. Underwater EMG Sensing
Another novel application of the insulated capacitive electrode interface is underwater EMG monitoring [117]. Coating
the electrodes with a thin water-proof insulating material (e.g.,
Fig. 14) prevents them from short-circuiting due to ions in water, and hence enhances the selectivity of the electrode coupling
to the physiological voltage source (i.e., muscles in [117]) evenin the case where the target source is immersed in conductive
liquid.
C. Wearable ECG and EEG Systems
Mobile applications of wireless noninvasive physiologicalsensing include wearable systems for continuous ambulatory
biopotential monitoring, made possible by advancements inelectronics miniaturization, low noise dry sensors and innovative

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1533

covered: pseudoresistors, chopping, driven right-leg circuits,
impedance bootstrapping, and application-specific design forelectrode–tissue impedance measurements and photoplethysmography. Highly efficient data conversion and signal encoding
for digital transmission were also reviewed. Finally, examples ofwearable and unobtrusive systems for physiological monitoringin novel applications were presented. These innovations, andothers to come in the next decade, will continue to benefit frominterdisciplinary collaborations between developers and usersof the noninvasive physiological monitoring technology such as
new physiological signal modalities, innovative electrode interfaces, improvements in integrated circuit design methodologies
and silicon technologies, improved system-level design optimizing for human factors and ergonomics, and clinician and patientperspectives on medical diagnostics and personal healthcare.
Fig. 15. Examples of wearable dry electrode systems. (a) Dry-contact ECG
belt with low noise membrane-based sensor [118]. (b) High-density dry EEG
headset with flexible electrodes that can operate through hair [10]. (c) Simultaneous recording between wet (Ag/AgCl conductive gel) and the dry EEG
headset showing the high signal quality [10].

mechanical form factors. Fig. 15 show two examples of ECG
and EEG systems.
The ECG belt utilizes a linear array of membrane-based drycontact sensors. Unlike conventional metal-based electrodes,
the membrane signal utilizes a semipermeable surface which
encapsulates a hydrogel. The membrane allows for ionic conduction between the body, through the membrane and into thehydrogel. This buffers the electrochemical interface by providing a stable gel-metal layer away from the skin and reduces thesensor noise [118].
For EEG, head hair presents a challenge for dry electrodes
which must reach the scalp. A flexible electrode is made froman elastomeric material which bends to brush aside hair andavoids the need for hard metallic prongs. An array of flexible
sensors (up to 64) is placed within a mechanical headset that is
adaptable to variety of head shapes and sizes. All of the AFE,
ASP, ADC, DSP, and RF electronics are integrated onto theheadset enabling mobile EEG acquisition [10].
VI. CONCLUSION
The advent of ubiquitous wearable physiological monitoringpromises to reduce some of the rising healthcare costs affecting developed and developing nations. Customized applications
bring opportunities for better preventive care and increasing
public awareness of their own medical conditions. In this paper,
we focused on the integrated circuit techniques to improve theperformance and expand the applications of noninvasive physiological monitoring instrumentation and discussed the challenges
at the electrode–body interface. From these interface considerations, we developed system requirements for reliable signal acquisition in biopotential, electrode–tissue impedance, and spectrophotometric measurements. Some of the main techniques
for implementing CMOS subthreshold integrated instrumentation amplifiers in a low-noise and power-efficient manner wereACKNOWLEDGMENT
The authors would like to thank M. Mollazadeh, E. Greenwald, and N. Thakor for collaborations on neural and cardiovascular electrophysiology systems, Y. Fukuoka for analysis ofindirectly measured ECG and EMG signals, and T. Kerth forcontributions to design of and experiments with the Cognionics
64-channel wireless dry EEG headset.
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Sohmyung Ha (S’05) received the B.S degree (withsumma cum laude) and the M.S. degree in electrical engineering from the Korea Advanced Instituteof Science and Technology, Daejeon, Korea, in 2004
and 2006, respectively. He is currently working toward the Ph.D. degree from the Department of Bioengineering, University of California San Diego, La
Jolla, CA, USA.
From 2006 to 2010, he was with Samsung Electronics as an Analog and Mixed-Signal Circuit Designer for commercial multimedia devices. His research interests include integrated circuits and interfaces for implantable andwearable biosignal monitoring and actuation systems. He received the Fulbright
Fellowship from 2010 to 2012.

IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, VOL. 61, NO. 5, MAY 2014

Chul Kim (S’13) received the B.S. degree from theKyungpook National University, Daegu, Korea, in
2007, and the M.S. degree from the Korea Advanced
Institute of Science and Technology, Daejeon, Korea,
in 2009, both in electrical engineering. He is currently
working toward the Ph.D. degree from the Department of Bioengineering, The University of California, San Diego, CA, USA.
He was with SK HYNIX, Gyeonggi, Korea, as aPower Circuitry Designer for DRAM from 2009 to
2012. His research interests include designing analog
integrated circuits and power management ICs for biomedical applications.

Yu M. Chi (S’04–M’11) received the B.S. degree inelectrical engineering from the Johns Hopkins University, Baltimore, MD, USA, in 2007, and the Ph.D.
degree in electrical engineering from the Universityof California San Diego, La Jolla, CA, USA, in 2011.
He is currently the Chief Technology Officer
at Cognionics, Inc., San Diego, CA, USA, working on wireless and noncontact biopotential sensing
systems.

Abraham Akinin (S’13) was born in Caracas,
Venezuela. He received the B.S. degree in biomedical engineering and physics from the University ofMiami, Coral Gables, FL, USA, in 2010. He is currently working toward the Ph.D. degree from Department of Bioengineering, the University of California
San Diego, La Jolla, CA, USA, and the Institute for
Neural Computation, La Jolla.
His research interests include closed-loop medical
instrumentation and neuroprosthetics.

Christoph Maier (S’97–M’00) received the DiplomPhysiker degree from the University of Heidelberg,
Heidelberg, Germany, in 1995, and the Dr.sc. degree in electrical engineering from the Swiss Federal Institute of Technology Zurich, Switzerland, in
2000.
After time in industry, he joined the Integrated
Systems Neuroengineering laboratory at the University of California San Diego, La Jolla, CA, USA, as
Postdoctoral Researcher in 2010. His main researchinterests include interfaces for electrophysiologicalsignals and modeling neural networks in analog VLSI.

HA et al.: INTEGRATED CIRCUITS AND ELECTRODE INTERFACES FOR NONINVASIVE PHYSIOLOGICAL MONITORING

Akinori Ueno (M’03) received the Ph.D. degree inbiomedical engineering from Keio University, Yokohama, Japan, in 1999.
He is a Professor of electrical and electronic engineering at Tokyo Denki University, Tokyo, Japan,
and held a position as an Associate Professor of electrical and computer engineering at Tokyo Denki University, Saitama, Japan. He was a Visiting Scholar
at the Department of Bioengineering, University ofCalifornia-San Diego, La Jolla, CA, USA, from April
2013 to March 2014. His research interests include
biomedical instrumentation and intelligent human–machine interfaces.
Dr. Ueno received several research awards from the Japan Society of Medical
Electronics and Biological Engineering, the Society of Life Support Engineering, and the Society of Instrument and Control Engineers.

1537

Gert Cauwenberghs (S’89–M’94–SM’04–F’11) received the M.Eng. degree in applied physics from the
University of Brussels, Brussels, Belgium, in 1988,
and the M.S. and Ph.D. degrees in electrical engineering from the California Institute of Technology,
Pasadena, CA, USA, in 1989 and 1994, respectively.
He is currently a Professor of bioengineering at
the University of California, San Diego, CA, where hecodirects the Institute for Neural Computation, participates as a member of the Institute of Engineeringin Medicine, and serves on the Computational Neuroscience Executive Committee, Department of Neurosciences graduate program.
Previously, he was a Professor of electrical and computer engineering at Johns
Hopkins University, Baltimore, MD, USA, and a Visiting Professor of brain
and cognitive science at the Massachusetts Institute of Technology, Cambridge,
MA, USA. He cofounded and chairs the Scientific Advisory Board of Cognionics Inc. His research interest includes micropower biomedical instrumentation,neuron-silicon, and brain–machine interfaces, neuromorphic engineering, andadaptive intelligent systems.
Dr. Cauwenberghs received the National Science Foundation Career Awardin 1997, the ONR Young Investigator Award in 1999, and the Presidential
Early Career Award for Scientists and Engineers in 2000. He was Francqui Fellow of the Belgian American Educational Foundation. He was a Distinguished
Lecturer of the IEEE Circuits and Systems Society from 2002 to 2003. He continues to serve IEEE in a variety of roles, most recently Cochairing the IEEE
Biomedical Circuits and Systems Conference (BioCAS’2011) and the technicalprogram committee of the IEEE Engineering in Medicine and Biology Conference (EMBC’2012), both held in San Diego. He is a Senior Editor for the IEEESENSORS JOURNAL and the IEEE JOURNAL ON EMERGING AND SELECTED TOPICS IN CIRCUITS AND SYSTEMS, and Editor-in-Chief of the IEEE TRANSACTIONSON BIOMEDICAL CIRCUITS AND SYSTEMS.