'A lower limb exoskeleton control system based on steady state' existed methodology

Journal of Neural Engineering J. Neural Eng. 12 (2015) 056009 (14pp)

doi:10.1088/1741-2560/12/5/056009

A lower limb exoskeleton control system based on steady state visual evoked potentials No-Sang Kwak1, Klaus-Robert Müller1,2 and Seong-Whan Lee1,3 1

Department of Brain and Cognitive Engineering, Korea University, Anam-dong, Seongbuk-ku, Seoul, Korea 2 Machine Learning Group, Department of Computer Science, TU Berlin, Berlin, Germany E-mail: nskwak@korea.ac.kr, klaus-robert.mueller@tu-berlin.de and sw.lee@korea.ac.kr Received 1 April 2015, revised 1 July 2015 Accepted for publication 3 July 2015 Published 17 August 2015 Abstract

Objective. We have developed an asynchronous brain–machine interface (BMI)-based lower limb exoskeleton control system based on steady-state visual evoked potentials (SSVEPs). Approach.

By decoding electroencephalography signals in real-time, users are able to walk forward, turn right, turn left, sit, and stand while wearing the exoskeleton. SSVEP stimulation is implemented with a visual stimulation unit, consisting of five light emitting diodes fixed to the exoskeleton. A canonical correlation analysis (CCA) method for the extraction of frequency information associated with the SSVEP was used in combination with k-nearest neighbors. Main results.

Overall, 11 healthy subjects participated in the experiment to evaluate performance. To achieve the best classification, CCA was first calibrated in an offline experiment. In the subsequent online experiment, our results exhibit accuracies of 91.3 ± 5.73%, a response time of 3.28 ± 1.82 s, an information transfer rate of 32.9 ± 9.13 bits/min, and a completion time of 1100 ± 154.92 s for the experimental parcour studied. Significance. The ability to achieve such high quality BMI control indicates that an SSVEP-based lower limb exoskeleton for gait assistance is becoming feasible.

Keywords: brain–machine interface, electroencephalogram, steady state visual evoked potentials, exoskeleton control (Some figures may appear in colour only in the online journal) 1. Introduction

rhythms through motor imagery (MI) [9]. Imagination of limb movement (e.g. right/left hand or foot movement) produces distinctive lateralized patterns on the motor cortex that can be detected. Of similar importance are BMIs that are based on event related potentials (ERP), e.g. the highly popular P300 spellers based on visual or auditory paradigms (cf [10, 11]). Recently, efficient feature extraction and classification methods of MI and ERP have been introduced (cf [12–18]).

However, MI-based systems are limited by the comparatively low number of reliable control commands that can be decoded. In addition, there are a considerable number of users that are unable to control a MI-based BMI [19]. Also, in general, BMI studies are typically performed while the subjects are sitting. Any subject mobility creates challenging

Brain–machine interfaces (BMIs) are communication systems in which the user’s intention is conveyed to the external world through devices without involving the normal output pathways of peripheral nerves and muscles [1, 2]. Invasive and non-invasive BMIs have raised hope for patients suffering from motor disabilities (e.g., [3, 4]). Several research groups have developed BMI techniques, which allow users to control external devices such as wheelchairs (e.g., [5, 6]) or robot arms (e.g., [7, 8]).

One of the most popular neurophysiological signatures used in BMI research is the modulation of sensorimotor 3

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motion artifacts in the EEG signal (see [20]); moreover it is difficult for subjects to engage in MI while moving.

In the following, we will use an SSVEP based BMI since it can provide a relatively high signal-to-noise ratio (SNR) and information transfer rate (ITR) (e.g. [10, 21]). Each SSVEP command is associated with repetitive visual stimuli that have a distinctive frequency. The user’s attention can selectively focus on the visual stimulus, then an oscillatory SSVEP component manifests itself in the subject’s visual cortex that matches the frequency (and higher harmonics) of the attended stimulus. SSVEPs can be elicited by repetitive visual stimuli at frequencies in the 1 to 100 Hz range [23].

This frequency locking can be decoded in various manners; e.g. common spatial patterns (CSP) [12] can classify SSVEP with low error without time delay. However, long training times may be needed to achieve high accuracy [24]. Stimuluslocked inter-trace correlation (SLIC) is suitable for irregular stimulus patterns [25]. The least absolute shrinkage and selection operator (LASSO)-based SSVEP classifiers typically yield higher ITR than other methods [26]. The CCA method for multi channel SSVEP detection shows increased detection accuracy, since it can also recognize and harvest from harmonic frequencies [27].

Other research has shown the feasibility of electrical hand prosthesis control with SSVEPs for healthy subjects in which flickering lights were mounted on the surface of the neuroprosthesis [21], and patients with tetraplegia [22]. An orthosis could lift up a leg whilst sitting and was controlled using SSVEP [28]. Asynchronous BCI control using a highfrequency SSVEP was introduced [29]. Also, visual evoked responses (P300) [20] and SSVEP [30] were detected during walking. Brain activity accompanying cognitive processes during whole body movement while driving [31] or in working environments [32] was analysed.

Recent studies have proved the possibility of decoding the user’s walking intentions within a virtual reality environment [33], or using an exoskeleton [34–36]. Also applications of BMI systems in stroke recovery and rehabilitation (see [37]) and hybrid assertive limb systems [38, 39] have been developed. In addition, a recent study investigated the use of an fNIRS-BCI to detect the preparation for the movement of the hip in subjects who have suffered a stroke [40]. Other research showed that EEG has valuable information for the decoding of expressive human movement [41].

Although BMI systems have shown great success in many studies, a future translation of closed-loop neuroprosthetic devices from the laboratory to the market still requires better long-term device reliability, robustness and safety [42]. Furthermore, significant challenges still exist for the development of a lower limb exoskeleton that can integrate with the user’s neuromusculoskeletal system [43].

In our study on healthy subjects, we will use non-invasive EEG-based systems for controlling an exoskeleton. Here, EEG is advantageous due to its reliability, safety, ease of acquisition, and cost effectiveness compared with other modalities; furthermore the SSVEP paradigm shows remarkable robustness with respect to artifacts that originate from the exoskeleton and walking movements.

In our preliminary study [44], we reported on an elementary prototype of an SSVEP controlled exoskeleton for a few subjects in an offline experiment design. In this work, we provide a more elegant method for the online control of the exoskeleton despite its excessive EEG artifacts. In addition, offline and online experiments, experimental results, performance evaluations and extensive analysis are given.

In this paper, we therefore demonstrate that a lower limb exoskeleton control system can be successfully operated asynchronously using an SSVEP-based BMI. Exoskeletons can create severe artifacts that make free EEG recordings unfeasible. Only by virtue of the oscillatory nature of SSVEP can we decode user intention despite the highly unfavourable signal to noise ratio, as nicely shown in our offline evaluation.

Also, an asynchronous BMI system is difficult to implement due to rest or idle states when the user does not look at any flickering light. Hence, we implement a robust asynchronous BMI system including an adjustable threshold to detect and compensate for rest or idle state activities. In addition, an SSVEP-based BMI online control of an exoskeleton as a gait assistant robot is demonstrated.

2. Materials and methods 2.1. Subjects

Eleven healthy subjects, with normal or corrected to normal vision and no history of neurological disease participated in this study (age range, 25–32 years old; 11 males). All experiments were conducted according to the principles expressed in the Declaration of Helsinki. This study was reviewed and approved by the Institutional Review Board at Korea University [1040548-KU-IRB-14–166-A-2] and written informed consent was obtained from all participants before the experiments.

2.2. Components of the system

The SSVEP based exoskeleton control system consists of (i) a signal processing and (ii) a device control part (figure 1(a)). In (i) a PC receives EEG data from a wireless EEG interface (MOVE system, Brain Products GmbH, 8 Ag/AgCl electrodes [PO7, PO3, PO, PO4, PO8, O1, Oz, and O2]) in figure 2, analyses the frequency information, and then provides (ii) the control command to the robotic exoskeleton (Rex, Rex Bionics Ltd.). A visual stimulation unit which was controlled by a micro controller unit (Atmega128) presented visual stimuli using five LEDs. The EEG reference electrode is mounted on the FCz and the ground electrode on Fpz. All impedances were maintained below 10kΩ; the sampling frequency rate was 1 kHz. The acquired EEG data was transmitted using a 2 s sliding window size with a 0.5 s shift. A 60-Hz notch filter was applied to the EEG data for removing AC power supply noise.

2.2.1. Exoskeleton. The advantages of the exoskeleton used

in this study are that it is self balancing, self supporting and has programmed motions (e.g. walking, turning, sitting, 2

J. Neural Eng. 12 (2015) 056009

N-S Kwak et al

Figure 1. (a) Components of an SSVEP-based exoskeleton system. (b) State machine diagram. (c) Visual stimulation unit.

stand. The serial port baud rate is 115200 bps using an 8-bit format, no parity and one stop bit. All multi-byte data was sent/received using a little-endian (LSB first). The only functionality provided by the wireless interface is the remote control of the Rex joystick operation (i.e. North Full (NF), South Full (SF), West Full (WF), East Full (EF), Clock Wise (CW) and Counter Clock Wise (CCW)). We have designed a state machine diagram to intuitively describe the system’s behavior (figure 1(b)).

2.2.2. Visual stimulation unit. The visual stimulation unit is

made out of an arm stand, five LEDs and a micro controller unit. The arm stand (IK-208, Ilkwang Inc.), with a weight of 1.3 kg and a length of 65 cm, is freely deformable. On the head of the arm stand, four squared-shaped multi-chip high flux LEDs (DG-82A83C-001–5/S-3), with a luminous intensity of 6000 mcd, a peak wavelength of 0.26/0.28 nm and a white emitting color, were attached. Figure 1(c) shows the locations of each LED. Their continuous flickering allows a command for walking forward, turning left, standing, turning right, and sitting. Previous research showed a higher amplitude of SSVEPs in the frequency range of 10 to 15 Hz than for other frequency bands [45]. Hence, we selected frequencies of LEDs with a 2 Hz interval to minimize the influence of adjacent stimuli. Consequently, LEDs are operated in 9, 11, 13, 15, and 17 Hz respectively and with a 0.5 duty ratio.

Figure 2. Ground (Fpz), reference (FCz) and eight electrode layout

in 10–20 system.

standing and shuffling). It thus enables a person to move by joystick control or by wireless interface. The walking speed and the angle of turning degree of the exoskeleton are approximately 0.1 ms−1 and 30° respectively. The Rex wireless interface (FTDI FT232R USB serial interface) consists of paired units: a sender unit connected to the PC via a USB cable and a receiver unit mounted in the Rex arm

2.3. Signal processing

Canonical correlation analysis (CCA) is used for decomposing the EEG signal in order to extract stimulation frequency 3

Measurement and Control in Robotics, M. A. Armada, P. Gonzales de Santos, and S. Tachi (Eds.), Producción Gráfica Multimedia, PGM, Madrid, Spain, 2003, ISBN: 84-607-9693-0, pp. 121-126 A SuFET Based Either Implantable or Non-Invasive (Bio)Transducer of Nerve Impulses R. V. Sklyar, Space Sensing Instruments, Verchratskogo st. 15-1, Lviv 79010, Ukraine E-mail: r_sklyar@hotmail.com

Abstract The main goal is to develop methods and devices for living being- machine interaction in order to obtain input and output signals from brain and motor nerves to the external devices or organs and vice versa. For this reason an efficient and accurate method of transducing biosignals from sense organs to output voltage, or artificial control signals to motor nerves, in limbs is developed and explained in the paper. Interaction between living beings and automatic equipment for process or environmental control is also presented. The transducer circuits and the intelligent system are given analytical treatment.

1 Introduction. Biophysical signals, engineering and scientific applications Steady and rapid progress in the robotics field requires ever quicker and better human- machine interaction and the development of a new generation of interfaces for intelligent systems. Such advances give rise to markedly increased biophysical research on the one hand and the need for new bioelectronic devices on the other. As a result of such efforts the design of synthesized neuroelectronic devices is high on the agenda.

Transduction and measurement of biosignals are key elements of bioelectronic and biomechanic systems design. There are two means involved in signal transduction: 1) biochemical- by hormones and enzymes; 2) biophysical - by nerve impulses (ionic currents). Let us consider the biophysical ones as useful for the said combined systems design above. There are two values voltage and electric current which characterize the pathway of transduction.

1.1 Methods of biosignals measurement: noninvasive and implantable, electro/magneticand biosensors Voltage potentials of the living organism and its organs are measured by both implantable and external electric field probes of high sensitivity [1]. Information on organ activity is obtained by measuring biomagnetic signals. For such purposes a multi-channel high

temperature superconducting interference (high Tc SQUID) system for magnetocardiography (MCG) and magnetoencephalography (MEG) of humans, with high magnetic field resolution has been developed [2, 3].

The known amperometric techniques of biosignals involve the Renview bight realising method [4], and the second method of "biosensors typically rely on an enzyme system which catalitically converts electrochemically non-active analytes into products which can be oxidized or reduced at a working electrode which is maintained at a specific potential with respect to a reference electrode" [5]. The same method is applicable also to potentiometic measurements "that can measure substrates, inhabitors or modulators of the enzyme". The Renview method requires extra stimulating of the isolated nerve fibre and the other method needs additional reagents and applied voltage.

1.2 Biosignals application to sensing techniques and control systems Many sensing organs of different physical values have been discovered. The most recent of these was the finding that "the antennae of jewel beetles can detect substances emitted in smoke from burning wood" [6].

Taking this into account, the exploitation of animals and even insects (schedulled for close attention in NASA's near future space explorations) as "living sensors" could be a potential reality in the near future [7]. In that case a secure and reliable biosignals pick-up method will be of paramount importance.

On the other hand, such living objects could produce some control signals from their nervous systems directly.

The first confirmation of the finding was achieved in recent experiments on fish, rats, monkeys and even humans [8,9].

The introduction of a bioelectronic mechanism for direct limb control by artificial nerve impulse previously received (implantable or non-invasive) from the nervous system or synthesized will be the next logical step [10,11].

2 The transducer arrangement The extensively developed SQUID systems do not suit the robotic and brain-machine applications because

they need cryogenic installation (or cryocoolers which introduce vibrational and magnetic noise problems) around the whole device and have a small dynamic range of detecting signals [12]. It is clear that the implantable variant of such magnetometers is completely impossible.

That is why a method that conserves the best features of the superconducting device was developed for removing the pickup coil (PC) from cryogenic installation [13,14]; On the other hand, neuroelectronic systems for two-way interfacing of the neuronal and the electronic components by capacitive contacts and by field-effect transistors with an open gate was developed [15]. They observed a wide spectrum of transistor signals monitoring the activity of neurons from leeches, snails and rats. In order for the electronic component to function under cryogenic (superconducting) conditions, there are a number of methods to connect them in vivo through three types of contacts: biocompatible, flexible and with increased adhesion [16-18].

The main objective of the project is to combine the bioelectric nature of nerve impulses and synaptic currents between neighboring neurons with body-temperature PC and zero resistance input of the superconducting fieldeffect transistor (SuFET) device in order to obtain most advantageous organism (living being)- machine interface.

The implantable and non-invasive variants of the device are defined by the type of contacting PCs and/or SuFET channel(s) with nerve impulses and/or synaptic currents (see Table).

Table. The different arrangements of the transducer according to the detecting quantity.

Type Implantable Non-invasive Object Nerve PC(s) (wrapped PC(s) (wrap. fibers around), SuFET around limb(s) channel(s) (introduand bodyced into the fiber) spinal cord) Brain SuFET channel(s) PC(s) activity (introduced between (helmet type) neighboring neurons) 2.1 Nerve fibre as a source of vortical magnetic field The method of transducing the vortical magnetic field from the nerve impulses by the PC wrapped around the nerve fibre was advanced long ago [19]. By introducing the said superconducting magnetometer with roomtemperature PC (SIM) [13,20] it is possible to create the implantable transducer (Fig. 1).

A PC with inductance L, self-capacitance C0 and active resistance R is connected in parallel with the drain of a SuFET cryogenic device. The SuFET is used as a zero-resistance ammeter which converts drain currents (I0 >Ic ) into gate voltages [20]. The critical Josephson current Io should be determined using experimental data and Ic is defined by the e.m.f. Eo from the expression:

Nerve fibre

head sensor R

…

E0

~

L

SuFET Draine Cin/2

gDS

× JJ

C0

H

- +

Rgate

Gate

°

Cin/2

VGS

Source

Cryostat

Shield

JJ- indicates Josephson junction.

Figure 1. Equivalent circuit of a SuFET based transducer of vortical magnetic field H.

Ic =

E0 ωH = µ 0 S eq zc zc

(1)

where Seq=µeff πd2N/4 with µ0-the permeability of free space, µ0=4π·10-7 henry/meter; µeff-the effective relative permeability of a high-µ metal core; d-the average diameter of a coil; N-total turn number of solenoid; zcimpedance of PC. In view of the dependence between gate-to-source voltage VGS and drain current [20]. This relationship is called transfer function and is defined as:

VGS =

(

QG hω T (QG ) + 1 − 1 − (I c / I 0 ) 2 C in 2e

)

(2)

where QG- the average gate charge and ωT is closely related to the small signal transconductance of the SuFET at VDS =0. Its sensitivity is maximized at Ic=Io/√2; however, the point of maximum current sensitivity can be adjusted by adding a dc external magnetic field [20].

Taking into account Eq. 1 we have:

VGS =

jπµ 0 S eq hω T (QG ) f eI 0 z c

H

(3)

Taking into account the noise sources, the equivalent circuit of the SIM takes the form as shown in [13]. The main influence on SIM’s sensitivity are made by current and voltage noise sources in LF, MF and HF ranges and the general equation is simplified as the following:  H SIMl ≅ 4k (TR + TSuFET γ noiseVc / I 0 ) /  µ 0 S eqω , when ω << ω 0 ;  4kTK ed + I 22 (CVGS ) sin 2 2ΘL2 / 4  , H ≅ SIMm  µ 0 S eq  H SIM =  2 2 when R << ωL, ω LC0 ≅ 1, R / ω << K ed ;  4kTSuFET γ noiseVcω 2C02 / I 0 + H µ 0 S eq , SIMh ≅ L  I 22 (CVGS ) sin 2 2Θ / 4   when ω 2 LC0 >> 1. where k- the Boltzmann constant; T- absolute temperature of copper wire; γnoise is the ratio of the

kinetic energy of the JJ link to thermal energy and VC characteristic voltage; sin2θ distortion of the pure Josephson sinθ term which is caused by the fluxdependent gate voltage effect with θ=2φDS/ћ in terms of the drain to source flux; I2=ħωT2C/2e.

2.2 Nerve fibre and neighboring neurons as a transmission line The main informational flux from organs of the senses to motor nerves is transmitted through nerve fibres which consist of a myelin shield with axons as a core. Recent research results suggest that such an arrangement is similar to a transmission line [21]. Synaptic currents between first order neighboring neurons into in vivo or brain slice preparations have an order of 50 pA [22].

The nerve impulses passing through the fibre could be unambiguously defined by detecting the matching ionic current(s) or its superposition. Such a technique seems optimal because even precise voltage measurement could not give a current value according to the Ohm law. First of all, nerve fibre must be separated from a living organism for resistance of fibre measurement and, secondly, this resistance may vary in time.

There are two feasible courses for impulse detection.

The first one is measuring the magnetic flux around the fibres by PC [19]. The second one is to let the impulses pass through the electronic device with minimal impediments.

Proceeding from the previously mentioned difficulties, including SuFET device into nerve fibre [15] seems to be the solution to the problem. Ionic currents in axons labeled ID and passing through the SuFET channel induce the output voltage Vg on its gate (Fig.2).

In any case the SuFET device should not introduce either losses of the nerve impulses or interferences or disturbances to them because of the superconducting nature of the transducer operational mode. Following signal processing and transmitting units which are completely separated from the living organism shouldn't interact with it due to the passive mode of transduction.

Let us consider the connection between the input (biosignal ID) and output (voltage Vg ) variables of the transducer (Eq.3):

Vg =

jhωT (QG ) ID 2eI 0

Taking into account noise parametres ( en and in ) it is possible to calculate the sensitivity limit of the transducer in the wide frequency range. With the SuFET device, noise current in is very small and may be neglected [13]. The resulting sensitivity of the transducer could be defined roughly as:

En = kT / g DS ⋅ γ noise , g DS = I 0 / Vc At the same time, the frequency of the transducer ranges from DC to high frequencies (limited by current loss on spurious capacitance). In such a case their will be current through axons- myelin- tissue capacitance Ct. For the advanced device the upper frequency limit can be defined as follows:

ϖup =

1 + R f / RDC Rf = L f Ct RDCL f Ct

where Rf, Lf- resistance and inductance of the nerve fibre respectively; RDC- the convey contact junction resistance.

Simulated performance data of the transducer in comparison with known preamplifiers of biosignals are myelin shown in Fig.3: Procesa) comparison of the measured and simulated sensitivity channel sing, nerve thresholds of different instrumentation amplifiers (IA) Vg impulse transtransmitvisualization for biomedical purposes, receiving ting and memory IA; unit ∗- a CMOS low- power low- noise monolithic mitting, unit units ionic ο- high performance, FET input IA; currents visualiza- voltage- sensitive differential input IA; gate tion, etc.

SFET • - an SuFET based transducer (Vc= 100µV, I0= 100µA, SuFET T= 126 K, γn= 20) [20]; units ■ a low- noise CMOS preamplifier for SQUIDs (T= axons (Bio)Telemetry 4K); ◊ - electrochemical PH- sensitive transducer and external in vivo following external units amperometric biosensor [5]; Figure 2. SuNerve- the inclusion of a SuFET device b) evaluation of the transducers along the design axis into thebased nerve fibre Fig. 1. An SFET biotransducer and its signal behavior [23].

It is supposed that the ionic current will be less than 2.3 Future trends of transducer development the critical current of the superconducting channel. "For There are some channels of SuFET development nonzero drain voltages, the SuFET absorbs lowwhich are highly important for the further improvement frequency power and re- emits this power at extremely of transducer performance. First of all there is the high frequencies" [13]. The detection of such a signal integration of high-temperature superconductivity with gives information about input current ID. Moreover, electronic devices. Such a SuFET device is being investivoltage could be applied at the gate in order to adjust gated with continuing progress [24]. Moreover, recent critical current.

Figure 3. Sensitivity and design merits of the biotransducers achievements in high temperature superconductivity verify the promising nature of the trend [25]. It is clear that the elimination of the need for a refrigeration system could mean a cheaper product that could be massproduced.

Another developmental trend is related to the adoption the transducer to living organism conditions via the introduction of organic superconductivity. A FET device based on these technologies has been studied in-depth [26]. Synthesized organic superconductors might be friendly to organisms and effective as a part of the electronic device. Ideally, high- temperature organic superconductor based SuFET device seems suitable for the above mentioned transducing technique. Increasing the suitability of the electronic devices was mentioned above. Implantation could be achieved by employing organic[27], diamond [28] and CNT [29] based (superconducting) electronic devices.

3 A biotransducer based sensing and control system The above arrangement seems quite suitable for use as a living object-machine interface or as an element of the intelligent system. The system is based on two technologies. On the one hand it is based on the the Supertransducer Human The nervous system motor nerves

SFET based transduser

Insect

Translator

a) Human (animal, insect) - machine interface

Organs of the senses

Supertransducer

Controlled drives

Visualization Artificial intelligence

b) A six-stages intelligent system

or

controlled drives

Figure 4. The (bio)transducer based intelligent system Fig. 3. The biotransducer based intelligent system

transducer and the automatic equipment that follows and on the other on the sensory system or motor nerves in limbs of living organisms. The advanced system procedure is shown on Fig. 4 (upper). As a result living beings control drives by previously translated biosignals.

In the other variant, biosignals from organs of the senses or brain transduce directly into intelligent or robotic systems which, in such a way, pick up environmental information (Fig. 4 (lower). Both of the structures are subjects for further refinement of all the elements independently from one another.

4 Conclusions The invented biotransducer has the following fundamental improvements upon existing ones: a) the sign of the output voltage permits the determination of the direction of the input current passing through a single SuFET device; b) situating the reference electrode outside the living organism makes precise measurement possible; c) the capability to regulate the proportion of axons that are being investigated to the untouched ones- either the whole cross section of the nerve fibre or any part of it; d) the possibility to substitute the SuFET device or to adjust its ratings to comply with the conditions of the meas. process without repeatedly destroying nerve fibre; e) the transducer could be either implanted or noninvasive (like the MEG) with conversion in both directions; f) the combination of biocompatibility and tissue equivalence in both the diamond and protein-based (organic) FETs makes them naturally fit for implantation.

In what areas can detected nerve impulses be applied?

There are two basic applications: 1) process control and 2) the connection of artificial sense organs and limbs: a) artificial limbs function by picking up a biosignal off motor nerves and transducing it after translation to electromechanical drives. The multiplication of finalcontrol elements is possible after the preliminary stages;

b) lost or damaged organs of the senses could be substituted or complemented by similarly operating human, animal, etc. organs. Its output biosignals may be picked up by the transducer and injected into nerve fibres of the recipient after reverse changing; c) substitution of inoperative control or motor nerve centers by control biosignals simulation and transducing them to living organs as discussed above.

All in all the complete robotic system (see Appendix) consists of a living organism in a feedback relationship with automation execution which interact with the aid of the proposed transducers in order, for example, to control some technological processes according to the state of the external environment. There are two operating channels.

The first of these is between the sense organs and limbs.

The second is between artificial sensors and drives.

Between both channels a mutual flow of information exists by means of the explained external and implantable transducers. It is presumed that the nerve- machine interface will allow the close monitoring of flow data and the additional input of signals between exposure to physical environmental stimuli and the resultant action response.

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[26] Schön J. H. et al., “Ambipolar pentacene fieldeffect transistors and inverters”, Science, Vol. 287, pp. 1022-1023, 2000.

[27] Tanase C. et al., “Local charge carrier mobility in disordered organic field-effect transistors”, Organic Electronics, accepted Apr. 2003.

[28] Garrido J. A. et al., “Fabrication of in-plane gate transistors on hydrogenated diamond surfaces”, Appl. Phys. Let., Vol. 82, No.6, pp. 988-990, 2003.

[29] Nihey F. et al., “Carbon-nanotube FETs with very high intrinsic transconductance”, Jpn. J. Appl.

Phys., Vol. 42, Part 2, pp. L1288-L1291, 2003.

1

SuFET

S u F E T

S u F E T

SuFET

Appendix

International Scholarly Research Network ISRN Nanotechnology Volume 2012, Article ID 102783, 9 pages doi:10.5402/2012/102783

Review Article A CNTFET-Based Nanowired Induction Two-Way Transducers Rostyslav Sklyar Verchratskogo st. 15-1, Lviv 79010, Ukraine Correspondence should be addressed to Rostyslav Sklyar, sklyar@tsp.lviv.ua Received 15 December 2011; Accepted 28 February 2012 Academic Editors: C. A. Charitidis and J. Sha Copyright Š 2012 Rostyslav Sklyar. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

A complex of the induction magnetic field two-way nanotransducers of the different physical values for both the external and implantable interfaces in a wide range of arrays are summarized. Implementation of the nanowires allows reliable transducing of the biosignalsâ&#x20AC;&#x2122; partials and bringing of carbon nanotubes into circuits leading to examination of the superconducting transition.

Novel sensors are based on the induction magnetic field principle, which causes their interaction with an ambient EM field.

Mathematical description of both the signal and mediums defines space embracing of the relevant interfacing devices. As a result, a wide range of the nano-bio-transducers allow both delivering the variety of ionized biosignals and interface the bioEM signals with further stages of electronic systems. The space coverage and transducing values properties of the state-of-the-art magnetic interfaces are summarized, and directions for their future development are deduced.

1. Introduction: Biophysical Signals, Transducing, and Interface Applications A biosensor is a device that incorporates a biologically active layer as the recognition element and converts the physical parameters of the biological interaction into a measurable analytical signal [1]. Understanding of biosignalsâ&#x20AC;&#x2122; (BS) nature and properties of their mediums are a basis for effective design of magnetic interfaces (MIs). Rapid progress in the advancement of several key science areas including nanoscale interfaces has stimulated the development of electronic sensor technologies applicable to many diverse areas of human activity. For example, the conceptualization and production of electronic nose devices have resulted in the creation of a remarkable new sector of sensor technology resulting from the invention of numerous new types of olfactory-competent electronic sensors and sensor arrays [2].

The growing variety of biosensors can be grouped into two categories: implantable and external. In turn, the last one has two existing paradigms: wearable sensor and noncontact sensor. A wearable sensor had potential to be intrusive, and noncontact sensor methods may still be intrusiveness to a certain extent, while a noncontact sensor is limited in its capability of acquiring physiological signals [3]. Voltage potentials of the living organism and its organs are measured

by both implantable and external electric field probes of high sensitivity [4]. Information on organ activity is obtained by measuring biomagnetic signals. For such purposes a multichannel high-temperature superconducting quantum interference device (high Tc SQUID) system for magnetocardiography (MCG) and magnetoencephalography (MEG) of humans, with high magnetic field resolution, has been developed [5, 6].

The most current sensing devices give us the possibility to receive a full scale of both the internal and external control BS. The internal ones are picking up by polymeric microprobes, CMOS chips, and nanoneedles, while the external by electromyography and neuroprosthetic (electroencephalogram (EEG) and MEG) systems. Improving an informational capability of the interface is implemented by the application of the advanced superconducting transducer and electromagnetic (EM) transistor/memristor [7, 8]. These elements are arranged into the arrays of a different configurations and can cover the order of spaces from macro- to nanolevels.

There are a number of methods and devices for transducing different BS into recordable or measurable information.

The transfer of nerve impulses (NI) is the main data flow that carries sensory information to the brain and control signals from it and from the spinal cord to the limbs.

ISRN Nanotechnology

5

Brain, spin. cord Tensor Vector Coil

Space dimens.

AND

Output

OR

Interface

Input

Array

Membrane

Scalar Plane

Channel

Curve Line Processor

Figure 5: Interaction of the natural and artificial processing bodies trough a SuFETTr-based interface.

voltage, or a concentration of organic or chemical substances (Figure 1). Moreover, this process can be executed in reverse.

Substances and/or voltages influence BS, thereby controlling or creating the said media (Figure 2).

As a result, we have achieved SuFETTr that is suitable for ascertaining the variety of values. Two directions of SuFETTr function enable decoding of the NI by comparing the result of its reaction on some process or organ with an action on them of the simulated electrical or biochemical signal after their reverse transducing through the SuFETTr(s) [44].

3.2. Arrangement of PSs in Arrays. An MF sensing array consists of primary sensors and, optionally, reference sensors.

Primary sensors use flux transformers located in a close proximity to the scalp or chest surface, where they couple to the brain’s or heart’s MFs, respectively [7, 45]. The reference sensors are used to subtract the environmental noise from the primary sensor outputs. The flux transformer design dictates its relative sensitivity to near and distant sources. Thus, the primary flux transformers can, in addition to detecting the brain signals, also provide various degrees of the environmental noise rejection. Flux transformers (magnetometers) have the highest sensitivity to both near- and far-field sources. Thus, they do not reduce the environmental noise (and must rely solely on the references or other techniques for the noise cancellation) [16].

Wire-wound gradiometers are the most conventional and are commercially available for EM systems. The PCs of wire-wound gradiometers are wound in opposition and balanced so that a uniform field links the zero net flux [37]. Conventional gradiometers, such as wire-wound, thin-film, or electronic gradiometers, are axial or planar, that is, “one dimensional” that detect the gradient of a MF in one direction. These one-dimensional gradiometers effectively reduce the ambient MF as their order increases.

However, they also reduce the biomagnetic signal. A “twodimensional” gradiometer detects the gradient of an MF in

Point Ions

Ferro Induct.

Phys. value

Dynam.

Figure 6: Interfacing ability (power) of the specific input elements.

It depends on two groups of space characteristics (form and direction) for the available transducing media.

two orthogonal directions to achieve high SNR. It focuses on a two-dimensional gradiometer that detects both the axialsecond-order and planar-first-order gradients of an MF. Figure 3 shows a PC for the two-dimensional gradiometer.

The discrete configuration space is a graph. Each node in the graph corresponds to an intermediate, and its neighbors are intermediates to which it can be folded or unfolded [38]. A common feature shared by folding of hydrophobicpolar chains on a lattice and self-folding a net through vertex connections is that in both cases the process of folding is driven by the formation of secondary links between topological neighbors (Figure 4). The best 2D arrangements, called planar nets, to create self-folding polyhedra with dimensions of a few hundred microns are determined, and optimal configurations for creating 3D geometric shapes have been found.

The importance of being able to address nanoscale elements in arrays goes beyond the area of nanocomputing and will be critical to the realization of other integrated nanosystems such as chemical/biological sensors. A regular crossed-NW FET array that consists of n-input Iin and moutput Uout NWs, in which outputs are the active channels of FETs and the inputs function as gate electrodes that turn these output lines on and off [46, 47].

3.3. Multisensor Data Fusion from Arrays. A further step should be synthesis of the said two methods in order to develop the external (nonimplantable) MCG&MEG signalsto-processor connection. The EM sensors are surface PCs, which are used in regular configuration where PCs with a small distance between each other are distributed under the heart or brain surface to pick up the local signals within the place of interest. The problem of sensing the EM signal for amplification/switching/memory with a speed of light in a

6

ISRN Nanotechnology Table 1: Dependence of the received BS parameters on the mode of SuFETTr’s functioning.

Mode

Medium

Serial External

NI Molecules DNA

!

!

ibio = 1cont. or sens. imp.

BSs → bio and chem. molec.

propagation of BS along DNA’s spirals

Parallel Implantable External ibio = ibio ( f1 ) + ibio ( f2 ) + dibio /dt, dibio /dx · · · + ibio ( fN ) " variation of BSs → BSs = 1 type of molec. concentr. of molec. decoding the BSs of space and length dynamic nucleoted recognition on both spirals

Implantable

"

"

ibio = 1 network or 1 fibre

BSs =

"

bio and chem. molec.

4 nucleoteds 4 outputs

Table 2: Measuring effects (values) and the relative nanotransducers (for interfacing).

single (passive) solid-state device EM transistor/memristor (EMTM) has been advanced [48] An attempt to lay down the foundations of biosensing by natural sensors and in addition to them by the artificial transducers of physical quantities, also with their expansion into space arrays and external/implantable functioning in relation to the nervous system, is performed. Because the sensing organs are exponentially better than any of analogous artificial ones, the advances in nanotechnology are opening the way to achieving direct electrical contact of nanoelectronic structures with electrically and electrochemically active neurocellular structures. The transmission of the sensors’ signals to a processing unit has been maintaining by an EM transistor/memristor (externally) and superconducting transducer of ionic currents (implantable). The arrays of the advanced sensors give us information about the space and direction dynamics of the signals’ spreading.

Recent developments in bioengineering, nanotechnology, and soft computing make it possible to create a new generation of intelligent sensing. There are developing opportunities for combining natural and artificial sensing abilities in the synthesized system. Backed up by the rapid strides of nanotechnology, nanosensor research is making a two-directional progress, firstly in evolving new sensors employing mesoscopic phenomena, and secondly in the performance enhancement of existing sensors. Nanosensors are nanotechnology-enabled sensors characterized by one of the following attributes: either the size of the sensor or its sensitivity is in the nanoscale, or the spatial interaction distance between the sensor and the object is in nanometers.

These nanosensors have been broadly classified into physical and chemical categories, with the biosensors placed on borderlines of biological signals with the remaining classes [49].

Multiprocessor data fusion is in effect intrinsically performed by animals and human beings to achieve a more

NW element PC(s) NIext EMTM induction transducer volume flowmeter

Superconducting membrane NIimpl acoustical EMTM noise absorb. universal flowmeter

A functional pattern of SuFETTr Informational flow Biological

EM field

Technical

el. current Electronic

Ionic el. voltage

Actuator

Ionic currents FerroEM Magnetic induction Magnetodynam.

SuFET channel NIimpl EMTM NIcontr gaseous flowmeter

Sensor

Physical value

Empirical data Biological

Technical Informational flow

Figure 7

accurate assessment of the processing environment. The aim of signal processing by the combined artificial-living being multiprocessor system is to acquire complete information, such as a decision or the measurement of quantity, using a selected set of input data streaming to a multiprocessor system-digital data are coming to artificial processor and the rest of information consumes by a neural system of living being (Figure 5). Thereby, a big amount of available information is managed using sophisticated data processing for the achievement of a high level of precision and reliability.

4. Results Application variety of the novel superconducting, organic, and CNTFETs allows us to design transducers of BS (nerve, biochemical, etc.) that transduce them into different quantities, including electric voltage, density of chemical and biomolecules. On the other hand, the said BS can

8 integrals respectively. The next two strings are explaining the bounds on the possible spreading of the said method.

The aggregated interface qualities, which are given in the tables, have been shown in the graph (Figure 6). Upon its analysis, it becomes clear that the area which is bounded by the dashed lines presents the MIs. At the same time, there are some uncertain areas (marked on the figure) that are inaccessible to the designed transducing elements.

Furthermore, the graph’s square is open to the right for perspective media of MIs.

5. Conclusions The reviewed variety of FETs shows the varying extent of readiness for them to be exploited in SuFETTr of electrical current signals (see Figure 7). The most appropriate for such an application are the ordinary solid-state SuFET modifications and novel CNT-based SuFETs. The organic SuFETs are not amply developed, but this work is being carried out in a number of directions. At the same time, the PCs, which are necessary for the external sensor with respect to the transducing medium (nerve fibre, flow of ions and DNA spiral), and corresponding low-ohmic wire traces for connecting PCs to the FET’s channel are sufficiently developed, even at nanodimensions.

The preliminary calculations confirm the possibility of broadening the SuFETTr’s action from magnetic field to the biochemical medium of BSs. The main parameters of such BSs can be gained by applying the arrangement of the SuFETTr(s) to the whole measurement system. Two directions of SuFETTr function enable decoding of the BS by comparing the result of its action on some process or organ with an action on them of the simulated electrical or biochemical signal after their reverse transducing through the SuFETTr(s). Furthermore, this decoded signal will provide a basis for creating feedback and feedforward loops in the measuring system for more precise and complete influence on the biochemical process.

The advance in the instrumentation techniques and technology of materials allows introducing of more accurate methods of interfacing and transducers for their execution (see Figure 8). At this level of progress, the head sensors of paramount sensitivity and simplicity in picking up function with minimal changes of the physical variables. The recent breakthrough in superconducting- and nanotechnologies caused the creation of induction transducers, which have better informational capability in some diagnostical purposes.

These devices are based on the universal law of the EM induction on the one hand and different special effects of MF interaction with a medium on the other. Since the proposed variety of bio-nano-sensors are passive, they do not affect the functions of the organs and their interaction.

References [1] F. Lucarelli, G. Marrazza, A. P. F. Turner, and M. Mascini, “Carbon and gold electrodes as electrochemical transducers for DNA hybridisation sensors,” Biosensors and Bioelectronics, vol. 19, no. 6, pp. 515–530, 2004.

ISRN Nanotechnology [2] A. D. Wilson and M. Baietto, “Advances in electronic-nose technologies developed for biomedical applications,” Sensors, vol. 11, no. 1, pp. 1105–1176, 2011.

[3] Y. Lin, “A natural contact sensor paradigm for nonintrusive and real-time sensing of biosignals in human-machine interactions,” IEEE Sensors Journal, vol. 11, no. 3, pp. 522–529, 2011.

[4] K. T. Ng, T. E. Batchman, S. Pavlica, and D. L. Veasey, “Noise and sensitivity analysis for miniature E-field probes,” IEEE Transactions on Instrumentation and Measurement, vol. 38, no.

1, pp. 27–31, 1989.

[5] H. Itozaki, S. Tanaka, T. Nagaishi, and H. Kado, “Multichannel high Tc SQUID,” IEICE Transactions on Electronics, vol. E77-C, no. 8, pp. 1185–1190, 1994.

[6] O. V. Lounasma, J. Knuutila, and R. Salmelin, “Squid technology and brain research,” Physica B, vol. 197, no. 1-4, pp. 54–63, 1994.

[7] R. Sklyar, “An EM transistor based brain-processor interface,” in Nanotechnology 2009: Life Sciences, Medicine, Diagnostics, Bio Materials and Composites, vol. 2, chapter 3: Nano Medicine, pp. 131–134, CRC Press, Houston, Tex, USA, 2009, http://www.nsti.org/procs/Nanotech2009v2/3/T82.602.

[8] R. Sklyar, “Induction magnetic field biosensors: from the macro to nano dimensions,” in Proceedings of the 1st BioSensing Technology Conference, Bristol, UK, November 2009, paper P2.3.13 (2 pages) of the abstract book.

[9] A. Kandori, D. Suzuki, K. Yokosawa et al., “A superconducting quantum interference device magnetometer with a roomtemperature pickup coil for measuring impedance magnetocardiograms,” Japanese Journal of Applied Physics I, vol. 41, no.

2 A, pp. 596–599, 2002.

[10] R. Sklyar, “Superconducting induction magnetometer,” IEEE Sensors Journal, vol. 6, no. 2, pp. 357–364, 2006.

[11] P. Fromherz, “Electrical interfacing of nerve cells and semiconductor chips,” ChemPhysChem, vol. 3, pp. 276–284, 2002.

[12] Y. Cui, Q. Wei, H. Park, and C. M. Lieber, “Nanowire nanosensors for highly sensitive and selective detection of biological and chemical species,” Science, vol. 293, no. 5533, pp. 1289–1292, 2001.

[13] R. Sklyar, “From nanosensors to the artificial nerves and neurons,” in Proceedings of the Nanotechnology in Industrial Applications (EuroNanoForum ’07), pp. 166–168, CCD, Düsseldorf, Germany, June 2007.

[14] R. Sklyar, “Superconducting organic and CNT FETs as a Biochemical Transducer,” in Proceedings of the 14th International Symposium on Measurement and Control in Robotics (ISMCR ’04), IEEE, Houston, Tex, USA, September 2004.

[15] R. Sklyar, “A SuFET based either implantable or non-invasive (Bio)transducer of nerve impulses,” in Measurement and Control in Robotics, M. A. Armada, P. Gonzales de Santos, and S. Tachi, Eds., pp. 121–126, Producción Gráfica Multimedia, Madrid, Spain, 2003.

[16] J. Vrba and S. E. Robinson, “SQUID sensor array configurations for magnetoencephalography applications,” Superconductor Science and Technology, vol. 15, no. 9, pp. R51–R89, 2002.

[17] V. Zalyubovskiy, A. Erzin, S. Astrakov, and H. Choo, “Energyefficient area coverage by sensors with two adjustable ranges,” Sensors, vol. 9, pp. 2446–2460, 2009.

[18] X. Chen and O. Bai, “Towards multi-dimensional robotic control via noninvasive brain-computer interface,” in Proceedings of the ICME International Conference on Complex Medical Engineering (CME ’09), Tempe, Ariz, USA, April 2009.

[19] R. Sklyar, “Sensors with a bioelectronic connection,” IEEE

Copyright © 2007, IEEE

Sensors with a Bioelectronic Connection Rostyslav SKLYAR

Abstract: The method and devices (SuFETTrs) for design of the bioelectronic sensors has been proposed. The method is based on combining the artificial sensors and organs of the senses with a nerve system of living beings for receiving recalibrated output signals. The circuits consist of the superconducting organic or solid-state field-effect transistor (SuFET) connected to a nerve fibre by the low-ohmic or nanotubes contacts. Application of organic, chemical and carbon nanotubes (CNT) based FETs for design of SuFETTrs is the proposed area of research. The range of picked up signals varies from 0.6 nA to 10 µA with frequencies from 20 to 2000 Hz. The output signal lies in the range of –5÷5V, (7÷0)⋅1017 /cm3 molecules and 2÷10 pH. The placement of the SuFETTr devices can be carried out both in vivo and in vitro with the possibility of forming the controlling signals s from the said quantities. Interaction between sensors and bioelectronic or mechatronic system in order to obtain input and output signals from brain and motor nerves to the external devices or organs and vice versa for processing or environmental control is also presented in the scheme.

Keywords: sensor, biosensor, organs of the senses, a living being, SuFET, bioelectronic, mechatronic

1

which for cNW-FET varies in the range 50 to 150 nS [34] and gives the order of ST 10-7 V/√Hz. Also for the noise voltage of parallel SuFETs based transducer is: 2

(EN ) (n)=4nkTSuFET γnoise/gdn VII. A SuFETTr based sensing and control system Critical to all mechatronic system architectures is the role of sensors, (actuators and other interfaces to the world within which the system exists and operates and that provide the measurement and control functions fundamental to any mechatronic system). Sensors are integral to mechatronic system as providers of both the process and procedural data on which operation is based [36].

Multisensor data fusion is in effect intrinsically performed by animals and human beings to achieve a more accurate assessment of the surrounding environment. The aim of signal processing by multisensor systems is to acquire determined information, such as a decision or the measurement of quantity, using a selected set of measured data stemming from a multisensor system. Thereby, a big amount of available information is managed using sophisticated signal processing for the achievement of a high level of precision and reliability [12].

Supertransducer

input sensor’s Human signal

output control signal

The nervous system

SuFETTr

SuFETTr

motor nerves Insect

SFET based transduser

Translator

Bioelectronic Transducer (BEleTr) processes

a) Human (animal, insect) - machine interface

recalibration loop

;

brain’s signal

/

Artificial sensors

Controlled drives

Organs of the senses

Supertransducer

NaSmaTr

Visualization

Artificial intelligence

Translator

Environment or

; action loop

b) A six-stages intelligent system

four-stages intelligent system Fig. 3.AThe biotransducer based intelligent system Fig. 8 A BEleTr based intelligent system

11

controlled drives

Table. Dependence of the received EC parameters on the mode of SuFETTr’s functioning Mode

serial

Medium

parallel

external

implantable

!i =1cont. or

i =i (f1)+i (f2)+…

sens. imp.

+i (fN)

EC, NI

external

!i =1 network or di /dt, di /dx 1 fibre

variation of Mole-

!ECs=1 type of

!ECs"bio and chem. molec.

molec. molec.

propagation of

!ECs= !bio and chem.

ECs"concentr. of cules

implantable

molec.

decoding the BSs

space and length 4 nucleoteds → 4

DNA

BS along

of nucleoted

dynamic on both

DNA’s spirals

recognition

spirals

outputs

Exploitation of the parallel input to SuFETTr allows determination of space and time dynamics of BSs in the nerve fibre and DNA spiral(s) and also the amplification of output signal Uout by multiplying the concentration of molecules according to a number of input BSs. After the implantation of parallel SuFET(s), the averaging or summation of this dynamic among the whole neural network, nerve fibre or DNA spiral(s) is possible.

IX. Conclusion The invented biotransducer has the following fundamental improvements upon existing ones: a) the sign of the output voltage permits the determination of the direction of the input current passing through a single SuFET device; b) situating the reference electrode outside the living organism makes precise measurement possible; c) the capability to regulate the proportion of axons that are being investigated to the untouched oneseither the whole cross section of the nerve fibre or any part of it; d) the possibility to substitute the SuFET device or to adjust its ratings to comply with the conditions of the measurement process without repeatedly destroying nerve fibre;

13

SuFET

S u F E T

S u F E T

Fig. 9 SuFETTr in the bioelectronic and mechatronic system 14

SuFET