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Progress in Brain-Compatible Interfaces with Soft Nanomaterials

Yong-Cheol Jeong, Han Eol Lee, Anna Shin, Dae-Gun Kim, Keon Jae Lee,* and Daesoo Kim*

Y.-C. Jeong, A. Shin, D.-G. Kim, Prof. D. KimDepartment of Biological ScienceKorea Advanced Institute of Science and Technology (KAIST)291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of KoreaE-mail: [email protected]. H. E. Lee, Prof. K. J. LeeDepartment of Materials Science and EngineeringKorea Advanced Institute of Science and Technology (KAIST)291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of KoreaE-mail: [email protected]

The ORCID identification number(s) for the author(s) of this article can be found under https://doi.org/10.1002/adma.201907522.

DOI: 10.1002/adma.201907522

Although the basic principles of neural prosthetics have not changed, the specificity and biocompatibility of neuro-medical devices has been improving as newer and more sophisticated materials are developed.[19,20] Conventional neural interfaces made of bulky, flat, and rigid materials have restricted in vivo applica-tions because they damage living tissues, especially in the brain. Recently, however, softer and more flexible materials (e.g., polyimide [PI], polyethylene terephthalate [PET], and polydimethylsiloxane [PDMS]) are being used in the production of neural implant devices to make them more

compatible with the curved and easily-damaged surface of the brain.[21–23] Even after achieving brain compatibility, there are other challenges to using soft materials in neural interfaces. First, soft materials must be compatible with various neural interface modules that have been optimized for use on a solid base. Second, the final product housing these modules on soft materials should still be flexible enough to maintain compat-ibility with the curved brain surface. These issues are more prominent in the generation of neural interfaces that include an LED module as a light source for optogenetics.[24] Recent studies have shown, however, the potential for flexible and brain-compatible materials that include micro-sized light-emit-ting diode (µLED) modules[25–27] as optogenetic light sources. In addition, the usage of these flexible materials has been extended in the production of devices that control drug release to specific brain areas.[28]

Over the last 10 years, flexible electronic devices have become more prominent in the manufacture of neural inter-faces that are more compatible with a variety of brain structures and functions. These include the flexible, paper-like brain sur-face wrapping electrode array, which can read information from a wide-range of cortical surfaces. Significant strides have also been made with the insertable wrapping electrode beneath the skull (iWEBS)[29] and the flexible vertical µLED (f-VLED) system insertable beneath the skull (iLEBS), which can be used to write information on the brain[30] (Figure 1). There are also insert-able devices for stable, precise, and controlled drug release on cortical surfaces[28] (Figure 1). In addition to the interfaces themselves, progress has been made in the development of self-powered flexible energy harvesters[31] and closed-loop and feedback control systems for neural interfaces[32] that make them more useful in behavior or symptom-specific control of the devices listed above (Figure 1). These applications will

Neural interfaces facilitating communication between the brain and machines must be compatible with the soft, curvilinear, and elastic tissues of the brain and yet yield enough power to read and write information across a wide range of brain areas through high-throughput recordings or optogenetics. Biocompatible-material engineering has facilitated the development of brain-compatible neural interfaces to support built-in modulation of neural circuits and neurological disorders. Recent developments in brain-compatible neural interfaces that use soft nanomaterials more suitable for complex neural circuit analysis and modulation are reviewed. Preclinical tests of the compat-ibility and specificity of these interfaces in animal models are also discussed.

1. Introduction

The emerging field of neural prosthetics uses neuromedical devices to improve the lives of in patients with missing or dam-aged sensory and motor functions. Since Galvani’s discovery of electrical conduction in frog legs in 1791,[1] the potential for neuromedical devices capable of modulating the electrical properties of neurons became apparent.[2] In patients with dam-aged or degenerated cochlear or retinal receptors, cochlear and retinal implants can transform sound and light stimuli into electrical signals, facilitating their perception.[3,4] Direct elec-trical stimulation of specific target areas via so-called deep brain stimulation (DBS) has also been applied in a wide range of neu-rological disorders, including Parkinson’s disease,[5,6] essential tremor,[7,8] dystonia,[9–11] obsessive-compulsive disorder,[12,13] epilepsy,[14,15] and major depression.[16] Recently, closed-loop interfaces between brain activity and motor outputs have even been used in efforts to cure severe neurological disorders like spinal cord injuries.[17,18]

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provide insight into the future development of bio-compatible neuromedical devices for improving the quality of life for patients with neurodisorders.

2. Brain-Compatible Reading Interfaces

Extensive brain mapping projects made it clear the cerebral cortex is responsible for the control of sensory and motor func-tions via its extensive connections with subcortical areas.[33–35] Thus, the ability to accurately measure cortical activity is crit-ical for obtaining in-depth information on the mechanisms of target brain areas and the mechanisms by which the symptoms of patients with neurological disorders arise. Electroencepha-lography (EEG),[36,37] functional magnetic resonance imaging (fMRI),[38,39] and magnetoencephalography (MEG)[40,41] are all used to monitor large-scale brain activity, but these imaging technologies are limited in their ability to precisely localize a signal’s origin.[42] Also, fMRI and MEG need imaging devices for working, of which the sizes and the weights are obstacles for using them in freely moving animals.[43] Thus, the animals should be anesthetized[44] or head-fixed,[45] which are making more difficult to study behavioral relationships with the brains. Recently, new tools of intracortical electrodes have been sug-gested to understand the massive brain activities. Electrocorti-cograms (ECoG), which are directional recordings of the cortical surface using an attached electrode, can be used to localize spe-cific cortical target areas.[46,47] Despite the prevalence of ECoG recordings, this technique has the following critical disadvan-tages. First, it requires removal of the skull, which can lead to unexpected damage.[48,49] Second, it provides information only during surgery, not in freely moving subjects. Third, ECoG suf-fers from irregular conductance across conventional electrodes due to the different degrees of contact with the curved cortical surface, producing inaccurate readings across target sites. Both PI- and silicone-based flexible electrode arrays have been applied in animals to solve these problems,[21–23] but it is difficult to sta-bilize larger area recordings while minimizing brain damage.

To get high quality neural information continuously without removing the skull, Park et al. proposed a PI-based flexible microelectrode array system called iWEBS, which can be inserted through very small cranial slits and then attached to the cortical surface for brain-mapping in free-moving animals (Figure 2a).[29] To make microelectrode arrays, gold-based wires are patterned onto a PI-based flexible substrate and then passivated with SU-8 epoxy, all of which are well-known biocompatible materials that can be used for long-term experiments (Figure 2b).[50–53] The thickness (14.5 µm) and stiffness (≈1.85 × 10−9 N m2) of the iWEBS was optimized for maintaining a fine-tuned balance between flexibility and rigidity. The iWEBS can efficiently pen-etrate the subcranial space, then conforming to the curved brain surface to make sufficient contact for efficient recordings. Using the iWEBS, researchers successfully measured whole cortical responses (Figure 2c) and functional connectivity in pharmaco-logical and optogenetic models of absence seizures (Figure 2d,e).

Other approach for reading brain activities is using ultrathin intracortical electrodes which could reduce mechanical brain damages. To generate brain-compatible and implantable ultrathin interfaces, various nano technologies and materials

are under development and are being tested in animal models.[54–58] Human applications, however, present many unique challenges. These technologies, including iWEBS, must be re-optimized for human brains, which are over 1000 times

Yong-Cheol Jeongs is cur-rently working as a doctoral candidate at Korea Advanced Institute of Science and Technology (KAIST). He received his B.S. in the Department of Biological Sciences at KAIST in 2012. After his research at Korea Institute of Science and Technology (KIST) in 2012, he joined an integrated M.S.,

and Ph.D. curriculum in the Department of Biological Sciences at KAIST. His research interests focus on circuit-based mechanisms of innate object/hunting behaviors and artificial intelligence.

Keon Jae Lee received his Ph.D. in materials science and engineering (MSE) at the University of Illinois, Urbana-Champaign (UIUC). During his Ph.D. at UIUC, he was involved in the first co-inven-tion of “flexible single-crys-talline inorganic electronics”, using top-down semiconduc-tors and soft lithographic transfer. Since 2009, he has

been a professor in MSE at KAIST. His current research topics are self-powered flexible electronic systems including self-powered sensors/energy harvester, micro LEDs, neu-romorphic memory/large-scale integration (LSI), and laser material interaction for in vivo biomedical applications.

Daesoo Kim received his Ph.D. in genetics and neu-roscience from POSTECH, Korea, in 1998. After post-doctoral training at the State University of New York Medical School at Brooklyn, he joined KIST from 2001 to 2004 as a senior researcher. Since 2004, he has been a professor in the Department of Biological Sciences at

KAIST. His research interests include the circuit-based mechanisms of innate behavior/movement disorder, 3D motion analysis using artificial intelligence, and brain–computer interface.

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larger[59] and which have more prominent gyri and sulci than the mouse brains in which these technologies were developed. In addition, extensive optimization of the physical properties of each interface has been done in animal models that is almost impossible to replicate in humans. When considering the level of individual variance that exists in humans, the development of human brain-compatible devices will require technology that permits the non-invasive measurement of the physical param-eters of target brain areas and the computational modeling of devices based on the resulting data.

3. Brain-Compatible Writing Devices

Of the various electronic devices (e.g., electrical, magnetic, and chemical devices), injectable optical systems have been investi-gated intensively as neural modulators because they cause very little heating or damage to the tissues, but they give stable, fast, and high-resolution responses, all while providing cellular level control of neural activity.[60–73] The study of this optical system, optogenetics, is a key technology used for the bi-directional modulation of neurons. Optogenetics can modify neuronal activity with high spatial and temporal resolution and with cell-type specificity[74] using light-dependent ion channels such as channelrhodopsins[24] or halorhodopsins.[75] Although electrical and chemical methods cannot match their temporal preci-sion, spatial resolution, and neuronal specificity, optogenetic techniques require a highly invasive genetic intervention that results in irreversible modifications to the nervous system.[76] Despite these limitations, however, many groups are working to apply optogenetic technology to the human brain.[77]

Some studies have employed various implantable optoge-netic stimulators with novel needle, fiber, and hybrid struc-tures.[73,78–80] Needle-type injectors make it possible to perform accurate localized stimulation of areas deep in the central nervous system. Montgomery et al. reported an injected wire-less optogenetic system, composed of a power receiving coil, a rectifying circuit, and a µLED.[81] Miniaturized optogenetic devices have been implanted in the right premotor cortex (M2) of Thy1-ChR2-EYFP-expressing mice. Then, in contrast to con-trol mice, these transgenic ChR2+ mice exhibited spontaneous circling movements during a 20 Hz-pulsed blue light stimula-tion. This device was also used to stimulate the spinal cord and peripheral nerve endings, successfully inducing three to eight times more c-Fos expression in ChR2+ mice than EYFP+ con-trol mice after 10 min of 10 Hz-pulsed light irradiation (light power density of 10 mW mm−2 and pulse width of 10 ms). Thus, injection-type optogenetic devices have been successfully used for accurate neural activation and behavior modulation in living mammals. In 2017, Shin et al. reported their development of a flexible wireless optogenetic stimulator for real-time mamma-lian behavior monitoring.[82] The stimulator was composed of blue/red µLEDs, a flexible antenna, a capacitor, and a rectifier. This allowed for high electrical/optical efficiency, outstanding output power, and wireless operation in a large experimental chamber.

These devices, however, all share serious drawbacks, including limitations on the size of the stimulation area and the requirement for a large craniotomy.[27,83–85] In addition, these implanted needle-type devices have to be fixed in soft neural tissues, and can cause severe mechanical/inflammatory damage in free-moving animals.[86–88] To solve the numerous

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Figure 1. Schematic showing bio-compatible materials for use in interface for the modulation of neural circuits and behavior. iWEBS, iLEBS, and f-DDM represent flexible reading, writing, and drug-delivery devices that cover the brain. The self-powered energy harvester represents a flexible energy harvester attached on the hinge joint. MIDAS represents a closed-loop control system including animal-behavior recording, behavior analysis, and wireless communication through a chip on the brain.



biomedical and structural issues with these devices, flexible thin-film electronic devices such as graphene-based carbon-layered electrode array technology,[89] opto-µECoG array,[90] transcranial optogenetic stimulation techniques,[91] flexible piezoelectric thin-film energy harvesters and nanosensors,[92] and trichogenic photostimulation using monolithic flexible vertical AlGaInP light-emitting diodes[93] have been proposed. These flexible and thin optogenetic devices are suitable for large-scale brain imaging, functional/cognitive behavior analysis, and for investigations into complex neural circuits due to their non-invasiveness, low weight, portability, outstanding biocom-patibility, excellent scalability, and ability to perform multiple simultaneous stimulations/recordings. Flexible optoelectronic devices have thus been spotlighted as next-generation neural modulating tools in the investigation of complex neural connec-tivity, behaviors, and neuropsychiatric therapies.[25,82,94–100]

Lee et al. manufactured flexible and ultrathin GaN-based µLED arrays for wireless optical brain stimulation (Figure 3a).[101] To minimize thermal brain damage during experiments, these flexible µLED devices were fabricated as high-efficiency, low-heating vertical structures. Furthermore, inspired by the iWEBS and considering functionally relevant factors such as mechan-ical tissue damage and efficient power transmission, the flexible µLED arrays were designed to be inserted into the subcranial space and efficiently illuminate a specific cortical domain.[102,103] This thin-film optical stimulator (15 µm thickness) was suc-cessfully inserted under a living mouse skull (Figure 3b) and used to illuminate the cerebral cortex with blue light without

any significant infection or inflammation (Figure 3c). In addi-tion, these flexible µLEDs (30 × 30 arrays in 1 × 1 cm2) can operate stably using a wireless power supply system, enabling continuous photostimulation in freely moving animals. GaN-based µLEDs exhibit remarkable stability to temperature at 85, 95, 105 °C at 85% humidity, providing an estimated lifespan of ≈12 years at room temperature. Frontal motor cortex stimula-tion with red light (10 Hz) in these mice evoked whisker and forelimb movements (Figure 3d,e). Since the red flexible µLED arrays have an optimized device stiffness of 2.6 × 10−8 N m and a thickness of ≈35 µm, brain-inserted µLEDs could emit enough power to activate neurons without any thermal or mechanical damage to the motor cortex. This was confirmed upon obser-vation of increased c-fos activity (Figure 3f). On the basis of these results, flexible thin-film photostimulators implanted in the intracranial space seem to be suitable tools for large-scale (≈1.2 × 1.0 cm2) and long-term (≈130 h) optogenetic manipulation of cortical activity and behavior.

4. Brain-Compatible Drug Delivery

Systemic drug administration leads to non-specific and unex-pected side effects because the drugs often act on non-target sites.[104,105] Thus, targeted drug delivery has emerged for the direct treatment of various disorders.[106] Lecomte et al. devel-oped a silk-fibroin-based flexible drug delivery system with supe-rior mechanical/electrical properties, long-term implantation,

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Figure 2. a) Schematic figure of iWEBS insertion for electrocorticogram (ECoG). b) Schematic figure of iWEBS with 100-µm-wide metal line and a 100 × 100 µm2 recording site (thickness: 12.5 µm of PI film, 200 nm of Au metal line, and 2 µm of SU-8). c) Mouse-brain atlas including motor, soma-tosensory, anterior cingulate, retrosplenial, and visual cortex. The black dots indicate the location of flexible electrodes, which were inserted under the skull. d) Schematic of flexible optogenetic mapping system with green-light irradiation. e) Light-induced spike from light stimulation. The spikes were measured by 12 channels in flexible electrodes. a–e) Reproduced with permission.[29] Copyright 2016, American Chemical Society.



and reliability in vivo.[107] This system, however, cannot precisely control the timing of drug release as it occurs through the dis-solution of a biodegradable membrane.[108] Temporal control of drug delivery is also important to minimize side effects, and it is currently being exploited in the field of diabetes with insulin delivery linked to glucose sensors.[109] Jeong et al. and Noh et al. reported a system for targeted and controlled drug delivery in the brain using controlled-release microchips in 2015 and 2017, respectively.[110,111] These brain stimulation systems, which inte-grate optogenetic and microfluidic devices, precisely release defined quantities of a drug to a specific brain region by moni-toring optogenetic signals. Although this system was success-fully used for an amalgamative optofluidic stimulation without severe brain tissue damage, it is difficult to implant on the cortex due to its bulky size and lack of flexibility.[112,113]

Sung et al. reported a flexible drug delivery microdevice (f-DDM) designed to be inserted into the subcranial space and stably attached to the cortical surface (Figure 4a).[28] This f-DDM was fabricated using biocompatible materials, including a flexible substrate, a polymer-based microres-ervoir, and a free-standing metal membrane.[114–117] A gold-based membrane was electrochemically dissolved using external electric current (1.1 mA cm−2), enabling the chemical delivery of the nanomaterial-based drugs from five different microreservoirs. The multiple reservoirs of the f-DDM can be filled with different drugs according to biomedical need. In addition, the quantity and variety of drug that is released could be individually controlled. To verify the practical appli-cations of this f-DDM in vivo, Sung et al. injected fluorescent

dyes, 1,1′-dioctadecyl-3,3,3′,3′-tetramethylindodicarbocyanine, 4-chlorobenzenesulfonate salt (DiD) and 1,1′-dioctadecyl-3,3,3′,3′-tetramethylindocarbocyanine perchlorate (DiI), into the microreservoirs, then slid the f-DDM slowly through the meningeal space without inducing any inflammatory or mechanical damage (Figure 4b). The volume of the injected DiD and DiI neurotracers were 0.058 and 0.053 mm3, respec-tively. The f-DDM freely controlled the drug release time by modulating input current density (1.1–5.6 mA cm−2). The drugs released from the f-DDM were localized and then they diffused vertically and laterally from the brain surface into deeper brain regions (maximum diffusion length, ≈0.3 mm; maximum dif-fusion area, 0.2 mm2). In this study, the authors verified the quantity of drug release from a fixed reservoir. For more prac-tical drug delivery applications, further research is necessary to improve the promptness and modulation of drug release as well as the life span of the devices in vivo.

5. Self-Powered Deep-Brain Stimulation and OptogeneticsA conventional brain stimulator needs repetitive surgeries to replace the battery roughly every 3 years depending on the site of stimulation.[118] These frequent invasive medical treatments are inconvenient and financially burdensome to patients. Thus, wireless energy sources are desirable because they would make devices more lightweight and improve freedom of movement. Haas et al. reported a novel wireless implantable electrical

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Figure 3. a) Photograph of the iLEBS under the mouse skull, showing the blue letters. b) Photograph of the iLEBS insertion surgery. c) Left: schematic figure of the coverage range of the iLEBS. Right: microscopy images showing the intact brain after iLEBS surgery. a–c) Reproduced with permission.[101] Copyright 2018, Wiley-VCH. d) Schematic figure of optogenetic stimulation for behavior modulation of mouse forelimbs and whiskers. e) Whisker movements during light stimulation by iLEBS. f) Confocal fluorescence images of chrimson, c-fos, and DAPI (4′,6-diamidino-2-phenylindole) after optogenetic activation. d–f) Reproduced with permission.[30] Copyright 2018, Elsevier.



stimulator for in vivo DBS applications.[119] This reusable stimulator was applied to several different animal behavioral models (i.e., open space, maze, and chamber environments), allowing for effective brain stimulation in free-moving mice. Xiang et al. reported a 3D mesh-type wireless brain stimu-lator, fabricated via a novel lithographic method using flexible biocompatible materials.[120] This device, comprising an analog/digital convertor, wireless transmitter, and an amplifier on a flexible substrate, was conformed to the curved and corru-gated brain surface and used to monitor chronic neural activity. However, these wireless powered stimulators show high-power consumption, overheating, and low energy-transfer efficiency. Therefore, work is ongoing to develop flexible, self-powered piezoelectric harvesters that transform mechanical vibrations and movement into electrical energy.[121,122]

In 2014, Hwang et al. reported a self-powered car-diac pacemaker that used a flexible single-crystalline Pb(Mg1/3Nb2/3)O3–PbTiO3 (PMNPT) thin-film energy har-vester,[123] but this device had insufficient power (2 V of oper-ating voltage) to activate brain neurons in vivo (3–5 V operating voltage). To adapt a self-powered flexible energy harvester to a DBS application, the thin-film material should have higher piezoelectric properties than those of previous work.[94,124–126] In 2016, Hwang et al. developed a high-performance yet flex-ible energy harvester using a second-generation relaxor piezo-electric single crystal. Hwang et al. grew a single-crystalline Pb(In1/2Nb1/2)O3–Pb(Mg1/3Nb2/3)O3–PbTiO3 (PIMNT) ingot using the Bridgman method, and then cut it into a thick plate. After grinding the PIMNT material into a film only ≈10 µm in thickness, the material maintained its higher piezoelectric properties. This PIMNT thin-film was transferred onto a PET substrate and connected to gold electrodes (Figure 5a). This enhanced flexible energy harvester exhibited a much higher output voltage of 11 V with a current of 285 µA, facilitating its application in living mice for in vivo DBS[31] (Figure 5b).

To modulate mouse behaviors via electrical stimulation, pointed electrodes were inserted into the primary motor (M1) cortex of the mouse brain. Then, electrical energy harvested by a flexible power generator was delivered to the forelimb muscle through the descending motor pathway. Figure 5c includes photographs of a live mouse whose paw movements were tracked

during DBS. The right forelimb of the mouse bent and unbent in response to electrical stimulation, as shown in Figure 5d. These results indicate that a self-powered neural electric stimu-lator can be stably implanted into a restricted cerebral region and functionally used to activate the M1 cortex of a live mouse.

6. Closed-Loop Control of Neural Circuits and BehaviorExperimental animal models have long been used as preclinical platforms for evaluating the biocompatibility and function-ality of neuromedical devices, but this is especially critical for brain-computer interfaces (BCI) because they demand real-time coordination between neural activity and behavior.[127] Beyond the feed-forward control systems of conventional BCIs, which relay neural signals to machines for machine-dependent behaviors,[128–131] newer feed-back or outcome-based systems are being developed for safer, more reliable, and more useful applications. For example, a BCI could be used to give patients control of a particular outcome only when specific symptoms arise. The range of applications for these feedback control systems will soon expand even to brain-to-brain[132] interfaces and beyond. Actually, the first brain–machine interface, based on neural responses, was developed by Vidal in 1973.[133] This was a noninvasive EEG experiment in which a subject followed a control object (cursor) on a computer screen. This kind of technology, which records brain signals and uses the resulting data to control a machine, is called a brain–computer Interface (BCI). BCI studies have employed various recording technolo-gies such as EEG, fMRI, and fNIRS.[134–136] Others have demon-strated the possibility of sending information directly to target brain areas through electrical stimulation. Yoo et al. created a brain-to-brain Interface (BBI) system that combines a BCI and brain stimulation. This could be used to establish a functional link between the brains of different species.[132] More recently, brain interface technology using optogenetic recording and stimulation technology has also emerged.

Park et al. developed a BCI linked to an object craving cir-cuit to control the motivation of mice as they followed a specific bait object.[32] This technology, referred to as medial preoptic

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Figure 4. a) Schematic of a flexible drug-delivery microdevice (f-DDS), which can be inserted beneath the mouse skull through a narrow cranial slit. b-i) Representative photograph of the insertion of f-DDM under the skull. The inset images show brain image and DAPI fluorescence image after f-DDM surgery. b-ii) Fluoresence microscopy cross-sectional images of a mouse brain, showing f-DDM insertion sites with two different fluorescent dyes (DiI: red; DiD: blue). a,b) Reproduced with permission.[28] Copyright 2018, Elsevier.



neuron-induced desire-assisted steering (MIDAS) allowed Park et al. to control mouse behavior via a head-mounted device containing an LED, a chipset to control the device, and a servo motor for guiding the presentation of the bait object (Figure 6a). A computer was used to deliver the location of the mouse subject and the bait object to the head-mounted device, which was then used to stimulate the object craving neurons via the LED module (Figure 6b). During the experiment, subject mice showed clear directional bait object chase behavior that

was dependent on photostimulation (Figure 7a). The timing of the stimulation from the MIDAS control system is determined by a closed-loop algorithm when the target object is located within the binocular visual field (Figure 7b,c). When the object moves outside the visual field, the mice explore other ambient objects non-specifically. Park et al. used the MIDAS closed-loop control system to guide mice through a complex maze faster than mice could navigate the maze without it (Figure 7d–f). This study proved the relevance of the closed-loop system in

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Figure 5. a) Photograph of flexible PIMNT energy harvester. b) Schematic figure of DBS of M1 motor cortex powered by flexible PIMNT energy har-vester. c) Representative figure of the forelimb movement tracking of the mouse’s forelimb during the DBS stimulation. d) Distance moved of the right forelimb after DBS. a–d) Reproduced with permission.[31] Copyright 2015, Royal Society of Chemistry.

Figure 6. a) Photograph of the head-mounted device and its components. b) Schematic figure of the concept of the MIDAS system. a,b) Reproduced with permission.[32] Copyright 2018, Springer Nature.



the optogenetic control of behavior and suggested its potential use in the treatment or management of neurological disorders like seizure,[137] Parkinson’s disease,[138,139] and depression[140] without causing side-effects due to over-stimulation or inhibi-tion of target brain areas.[141]

7. Summary and Outlook

For the past 10 years, the field of materials science has provided innovative materials for the manufacture of devices enabling the in vivo modulation of brain circuits and behavior. Here, we have highlighted recent developments in such materials that are compatible with the brain for use in neuroscience research and in the clinic. Flexible polymer-based devices provide a soft and flexible platform for use in brain-compatible neuromedical devices that can read and write information in the brain, har-vest kinetic energy for powering the devices, and much more. In addition to advances in materials, however, the design and development of the next generation of brain-compatible devices will also require extensive knowledge of neuroscience, from brain anatomy to neurophysiology. Brain-compatible interfaces still face many hurdles before the technologies can be adapted for use in human subjects. This is mainly because of differ-ences in size, structure, and physiology between human and animal brains. One of the limitations of current flexible brain-compatible interfaces is the low number of channels available for reading and writing from the whole brain. Many flexible

brain-compatible interfaces have 2D sheet structures, which can only read and write on the cortical surface. These structural limitations could be overcome by flexible thread structures,[142] and finally by a 3D mesh structure that covers the whole brain. The combination of optogenetics with other techniques will also present a huge challenge in the application to human sub-jects because optogenetics requires genetic modifications that can induce their own problems. Still, the immense potential of optogenetics in human applications has led to various clin-ical attempts.[143] Successful human clinical trials will increase opportunities for brain-compatible interfaces that include optogenetic components. Closed-loop control interfaces for regulating neural signals will be key for patient treatments, but there are still difficulties in the automated assessment of the symptoms of many disorders. Deep-learning-based behavioral detectors[144] combined with physiological symptom detectors will help improve the time, duration, and amplitude of a given treatment in real-time. Fortunately, as brain-related technolo-gies continue to improve, it becomes more and more possible to gain this understanding. This creates a virtuous cycle of tech-nological development that drives neuroscience forward and reduces the time gap to real clinical applications.

AcknowledgementsY.-C.J. and H.E.L. contributed equally to this work. This work was supported by the Creative Materials Discovery Program (NRF-2016M3D1A1900035 to K.J.L.), the Brain Research Program

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Figure 7. a) Schematic figure of the MIDAS configuration. b,c) Schematic figure and algorithmic workflow showing the LED on/off control and object direction control. d) Schematic figure of the MIDAS test maze and its obstacles. e) Left: Mice locomotion tracks and velocities when the MIDAS did not refine the time to stimulate the brain circuit. Right: Mice locomotion tracks and velocities when the MIDAS refined the time to stimulate the brain circuit to control the mice navigation. f) Maze-breaking success rate of the continuous and refined stimulation by MIDAS. a–f) Reproduced with per-mission.[32] Copyright 2018, Springer Nature.



(NRF-2017M3C7A1029612 to D.K.), and the Bio & Medical Technology Development Program (NRF-2019M3E5D2A01066259 to D.K.) through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT and Future Planning. This work was also supported by the Samsung Science and Technology Foundation (SSTF-BA1301-07 to D.K.).

Conflict of InterestThe authors declare no conflict of interest.

Keywordsbiocompatible materials, brain–computer interfaces, flexible electronics, neural circuit controls, neuromedical devices

Received: November 15, 2019Revised: February 3, 2020

Published online: April 16, 2020

[1] M. Piccolino, Brain Res. Bull. 1998, 46, 381.[2] S. A. Guillory, K. A. Bujarski, Soc. Cogn. Affect. Neurosci. 2014,

9, 1880.[3] J. T. Rubinstein, Curr. Opin. Otolaryngol. Head Neck Surg. 2004,

12, 444.[4] C. Sekirnjak, P. Hottowy, A. Sher, W. Dabrowski, A. M. Litke,

E. Chichilnisky, J. Neurosci. 2008, 28, 4446.[5] G. Deuschl, C. Schade-Brittinger, P. Krack, J. Volkmann, H. Schäfer,

K. Bötzel, C. Daniels, A. Deutschländer, U. Dillmann, W. Eisner, N. Engl. J. Med. 2006, 355, 896.

[6] A.-L. Benabid, P. Pollak, A. Louveau, S. Henry, J. De Rougemont, Stereot. Funct. Neuros. 1987, 50, 344.

[7] W. Ondo, J. Jankovic, K. Schwartz, M. Almaguer, R. Simpson, Neurology 1998, 51, 1063.

[8] E. D. Flora, C. L. Perera, A. L. Cameron, G. J. Maddern, Mov. Disord. 2010, 25, 1550.

[9] M. Vidailhet, L. Vercueil, J.-L. Houeto, P. Krystkowiak, A.-L. Benabid, P. Cornu, C. Lagrange, S. Tézenas du Montcel, D. Dormont, S. Grand, N. Engl. J. Med. 2005, 352, 459.

[10] R. Kumar, A. Dagher, W. Hutchison, A. Lang, A. Lozano, Neurology 1999, 53, 871.

[11] L. Vercueil, P. Pollak, V. Fraix, E. Caputo, E. Moro, A. Benazzouz, J. Xie, A. Koudsie, A.-L. Benabid, J. Neurol. 2001, 248, 695.

[12] B. Nuttin, J. Gybels, P. Cosyns, L. Gabriels, B. Meyerson, S. Andréewitch, S. Rasmussen, B. Greenberg, G. Friehs, A. R. Rezai, Neurosurgery 2002, 51, 519.

[13] B. D. Greenberg, D. A. Malone, G. M. Friehs, A. R. Rezai, C. S. Kubu, P. F. Malloy, S. P. Salloway, M. S. Okun, W. K. Goodman, S. A. Rasmussen, Neuropsychopharmacology 2006, 31, 2384.

[14] T. Loddenkemper, A. Pan, S. Neme, K. B. Baker, A. R. Rezai, D. S. Dinner, E. B. Montgomery Jr., H. O. Lüders, J. Clin. Neuro-physiol. 2001, 18, 514.

[15] S. Charbades, P. Kahane, L. Minotti, A. Koudsie, E. Hirsh, A.-L. Benabid, Epileptic Disord. 2002, 4, S83.

[16] T. E. Schlaepfer, M. X. Cohen, C. Frick, M. Kosel, D. Brodesser, N. Axmacher, A. Y. Joe, M. Kreft, D. Lenartz, V. Sturm, Neuropsy-chopharmacology 2008, 33, 368.

[17] M. Capogrosso, T. Milekovic, D. Borton, F. Wagner, E. M. Moraud, J.-B. Mignardot, N. Buse, J. Gandar, Q. Barraud, D. Xing, Nature 2016, 539, 284.

[18] C. E. Bouton, A. Shaikhouni, N. V. Annetta, M. A. Bockbrader, D. A. Friedenberg, D. M. Nielson, G. Sharma, P. B. Sederberg, B. C. Glenn, W. J. Mysiw, Nature 2016, 533, 247.

[19] K. Scholten, E. Meng, Lab Chip 2015, 15, 4256.[20] J. A. Fairfield, Adv. Funct. Mater. 2018, 28, 1701145.[21] B. Rubehn, C. Bosman, R. Oostenveld, P. Fries, J. Neural Eng.

2009, 6, 036003.[22] J. Viventi, D.-H. Kim, L. Vigeland, E. S. Frechette, J. A. Blanco,

Y.-S. Kim, A. E. Avrin, V. R. Tiruvadi, S.-W. Hwang, A. C. Vanleer, Nat. Neurosci. 2011, 14, 1599.

[23] B. A. Hollenberg, C. D. Richards, R. Richards, D. F. Bahr, D. M. Rector, J. Neurosci. Methods 2006, 153, 147.

[24] E. S. Boyden, F. Zhang, E. Bamberg, G. Nagel, K. Deisseroth, Nat. Neurosci. 2005, 8, 1263.

[25] T.-i. Kim, J. G. McCall, Y. H. Jung, X. Huang, E. R. Siuda, Y. Li, J. Song, Y. M. Song, H. A. Pao, R.-H. Kim, Science 2013, 340, 211.

[26] R. H. Kim, S. Kim, Y. M. Song, H. Jeong, Small 2012, 8, 3123.[27] S. I. Park, D. S. Brenner, G. Shin, C. D. Morgan, B. A. Copits,

H. U. Chung, M. Y. Pullen, K. N. Noh, S. Davidson, S. J. Oh, Soft, stretchable , Nat. Biotechnol. 2015, 33, 1280.

[28] S. H. Sung, Y. S. Kim, D. J. Joe, B. H. Mun, B. K. You, D. H. Keum, S. K. Hahn, M. Berggren, D. Kim, K. J. Lee, Nano Energy 2018, 51, 102.

[29] A. H. Park, S. H. Lee, C. Lee, J. Kim, H. E. Lee, S.-B. Paik, K. J. Lee, D. Kim, ACS Nano 2016, 10, 2791.

[30] S. H. Lee, J. Kim, J. H. Shin, H. E. Lee, I.-S. Kang, K. Gwak, D.-S. Kim, D. Kim, K. J. Lee, Nano Energy 2018, 44, 447.

[31] G.-T. Hwang, Y. Kim, J.-H. Lee, S. Oh, C. K. Jeong, D. Y. Park, J. Ryu, H. Kwon, S.-G. Lee, B. Joung, Energy Environ. Sci. 2015, 8, 2677.

[32] S.-G. Park, Y.-C. Jeong, D.-G. Kim, M.-H. Lee, A. Shin, G. Park, J. Ryoo, J. Hong, S. Bae, C.-H. Kim, Nat. Neurosci. 2018, 21, 364.

[33] M. Jeong, Y. Kim, J. Kim, D. D. Ferrante, P. P. Mitra, P. Osten, D. Kim, Sci. Rep. 2016, 6, 20072.

[34] R. N. Lemon, Annu. Rev. Neurosci. 2008, 31, 195.[35] N. Li, T.-W. Chen, Z. V. Guo, C. R. Gerfen, K. Svoboda, Nature

2015, 519, 51.[36] C. Braboszcz, A. Delorme, NeuroImage 2011, 54, 3040.[37] J. H. Choi, K. P. Koch, W. Poppendieck, M. Lee, H. S. Shin, J. Neu-

rophysiol. 2010, 104, 1825.[38] G. Buccino, F. Binkofski, G. R. Fink, L. Fadiga, L. Fogassi,

V. Gallese, R. J. Seitz, K. Zilles, G. Rizzolatti, H. J. Freund, Eur. J. Neurosci. 2001, 13, 400.

[39] J. O’Doherty, M. L. Kringelbach, E. T. Rolls, J. Hornak, C. Andrews, Nat. Neurosci. 2001, 4, 95.

[40] J. Liu, A. Harris, N. Kanwisher, Nat. Neurosci. 2002, 5, 910.[41] K. Marinkovic, R. P. Dhond, A. M. Dale, M. Glessner, V. Carr,

E. Halgren, Neuron 2003, 38, 487.[42] S. P. van den Broek, F. Reinders, M. Donderwinkel, M. J. Peters,

Electroencephalogr. Clin. Neurophysiol. 1998, 106, 522.[43] R. L. Buckner, F. M. Krienen, B. T. Yeo, Nat. Neurosci. 2013, 16, 832.[44] M. N. DeSalvo, U. Schridde, A. M. Mishra, J. E. Motelow,

M. J. Purcaro, N. Danielson, X. Bai, F. Hyder, H. Blumenfeld, NeuroImage 2010, 50, 902.

[45] M. Desai, I. Kahn, U. Knoblich, J. Bernstein, H. Atallah, A. Yang, N. Kopell, R. L. Buckner, A. M. Graybiel, C. I. Moore, E. S. Boyden, J. Neurophysiol. 2011, 105, 1393.

[46] W. Penfield, T. Rasmussen, Macmillan, New York 1950.[47] H. Jasper, W. Penfield, Epilepsy and the Functional Anatomy of the

Human Brain, Little Brown & Co, Boston, MA, USA 1954.[48] J. T. Cole, A. Yarnell, W. S. Kean, E. Gold, B. Lewis, M. Ren,

D. C. McMullen, D. M. Jacobowitz, H. B. Pollard, J. T. O’Neill, J. Neurotrauma 2011, 28, 359.

[49] K. Shapiro, A. Fried, F. Takei, I. Kohn, J. Neurosurg. 1985, 63, 76.

Adv. Mater. 2020, 32, 1907522



Adv. Mater. 2020, 32, 1907522

[50] C. Hassler, T. Boretius, T. Stieglitz, J. Polym. Sci., Part B: Polym. Phys. 2011, 49, 18.

[51] N. Lago, D. Ceballos, F. J. Rodríguez, T. Stieglitz, X. Navarro, Biomaterials 2005, 26, 2021.

[52] G. Kotzar, M. Freas, P. Abel, A. Fleischman, S. Roy, C. Zorman, J. M. Moran, J. Melzak, Biomaterials 2002, 23, 2737.

[53] S.-H. Cho, H. M. Lu, L. Cauller, M. I. Romero-Ortega, J.-B. Lee, G. A. Hughes, IEEE Sens. J. 2008, 8, 1830.

[54] J. E. Chung, H. R. Joo, J. L. Fan, D. F. Liu, A. H. Barnett, S. Chen, C. Geaghan-Breiner, M. P. Karlsson, M. Karlsson, Neuron 2019, 101, 21.

[55] C. Xie, J. Liu, T.-M. Fu, X. Dai, W. Zhou, C. M. Lieber, Nat. Mater. 2015, 14, 1286.

[56] X. Wei, L. Luan, Z. Zhao, X. Li, H. Zhu, O. Potnis, C. Xie, Adv. Sci. 2018, 5, 1700625.

[57] T.-M. Fu, G. Hong, T. Zhou, T. G. Schuhmann, R. D. Viveros, C. M. Lieber, Nat. Methods 2016, 13, 875.

[58] L. Luan, X. Wei, Z. Zhao, J. J. Siegel, O. Potnis, C. A. Tuppen, S. Lin, S. Kazmi, R. A. Fowler, Sci. Adv. 2017, 3, e1601966.

[59] R. D. Hodge, T. E. Bakken, J. A. Miller, K. A. Smith, E. R. Barkan, L. T. Graybuck, J. L. Close, B. Long, N. Johansen, O. Penn, Nature 2019, 573, 61.

[60] D. Oesterhelt, W. Stoeckenius, Nature, New Biol. 1971, 233, 149.[61] C. Song, T. Knöpfel, Nat. Rev. Drug Discovery 2016, 15, 97.[62] S. Yoo, J.-H. Park, Y. Nam, ACS Nano 2019, 13, 544.[63] L. Grosenick, J. H. Marshel, Neuron 2015, 86, 106.[64] B. R. Arenkiel, J. Peca, I. G. Davison, C. Feliciano, K. Deisseroth,

G. J. Augustine, M. D. Ehlers, G. Feng, Neuron 2007, 54, 205.[65] S. Waddell, Trends Neurosci. 2010, 33, 457.[66] T. Tzounopoulos, Neuron 2009, 62, 463.[67] A. M. Packer, B. Roska, M. Häusser, Nat. Neurosci. 2013, 16,

805.[68] E. Bullmore, O. Sporns, Nat. Rev. Neurosci. 2009, 10, 186.[69] M. N. Shadlen, J. A. Movshon, Neuron 1999, 24, 67.[70] D. J. Wang, K. Jann, C. Fan, Y. Qiao, Y.-F. Zang, H. Lu, Y. Yang,

Front. Neurosci. 2018, 12, 352.[71] A. Steude, E. C. Witts, G. B. Miles, M. C. Gather, Sci. Adv. 2016, 2,

e1600061.[72] B. Fan, W. Li, Lab Chip 2015, 15, 3838.[73] H. Xu, L. Yin, C. Liu, X. Sheng, N. Zhao, Adv. Mater. 2018, 30,

1800156.[74] K. Deisseroth, Nat. Methods 2011, 8, 26.[75] V. Gradinaru, K. R. Thompson, K. Deisseroth, Brain Cell Biol. 2008,

36, 129.[76] F. Gilbert, A. R. Harris, R. M. Kapsa, AJOB Neurosci. 2014, 5, 3.[77] S. Jarvis, S. R. Schultz, Front. Syst. Neurosci. 2015, 9, 157.[78] B. Fan, K.-Y. Kwon, R. Rechenberg, M. F. Becker, A. J. Weber, W. Li,

Technology 2016, 4, 15.[79] C. Lu, U. P. Froriep, R. A. Koppes, A. Canales, V. Caggiano,

J. Selvidge, E. Bizzi, P. Anikeeva, Adv. Funct. Mater. 2014, 24, 6594.

[80] S. I. Park, G. Shin, J. G. McCall, R. Al-Hasani, A. Norris, L. Xia, D. S. Brenner, K. N. Noh, S. Y. Bang, D. L. Bhatti, Proc. Natl. Acad. Sci. USA 2016, 113, E8169.

[81] K. L. Montgomery, A. J. Yeh, J. S. Ho, V. Tsao, S. M. Iyer, L. Grosenick, E. A. Ferenczi, Y. Tanabe, K. Deisseroth, S. L. Delp, Nat. Methods 2015, 12, 969.

[82] G. Shin, A. M. Gomez, R. Al-Hasani, Y. R. Jeong, J. Kim, Z. Xie, A. Banks, S. M. Lee, S. Y. Han, C. J. Yoo, Neuron 2017, 93, 509. e503.

[83] S. Park, Y. Guo, X. Jia, H. K. Choe, B. Grena, J. Kang, J. Park, C. Lu, A. Canales, R. Chen, Nat. Neurosci. 2017, 20, 612.

[84] P. Gutruf, V. Krishnamurthi, A. Vázquez-Guardado, Z. Xie, A. Banks, C.-J. Su, Y. Xu, C. R. Haney, E. A. Waters, I. Kandela, Nat. Electron. 2018, 1, 652.

[85] K. L. Montgomery, A. J. Yeh, J. S. Ho, V. Tsao, S. M. Iyer, L. Grosenick, E. A. Ferenczi, Y. Tanabe, K. Deisseroth, S. L. Delp, Nat. Methods 2015, 12, 969.

[86] J. Ohta, C. Kitsumoto, T. Noda, K. Sasagawa, T. Tokuda, M. Motoyama, Y. Ohta, T. Kobayashi, Y. Ishikawa, S. Shiosaka, in 2012 IEEE Biomedical Circuits and Systems Conference (BioCAS), IEEE, Piscataway, NJ, USA 2012, pp. 244–247.

[87] A. Schneider, L. Frasson, T. Parittotokkaporn, F. M. R. Y. Baena, B. L. Davies, S. E. Huq, Microelectron. Eng. 2009, 86, 1515.

[88] N. Vasylieva, S. Marinesco, D. Barbier, A. Sabac, Biosens. Bioelectron. 2015, 72, 148.

[89] D.-W. Park, A. A. Schendel, S. Mikael, S. K. Brodnick, T. J. Richner, J. P. Ness, M. R. Hayat, F. Atry, S. T. Frye, R. Pashaie, Nat. Commun. 2014, 5, 5258.

[90] K. Y. Kwon, B. Sirowatka, A. Weber, W. Li, IEEE Trans. Biomed. Circ. Syst. 2013, 7, 593.

[91] R. Hira, N. Honkura, J. Noguchi, Y. Maruyama, G. J. Augustine, H. Kasai, M. Matsuzaki, J. Neurosci. Methods 2009, 179, 258.

[92] G. T. Hwang, M. Byun, C. K. Jeong, K. J. Lee, Adv. Healthcare Mater. 2015, 4, 646.

[93] H. E. Lee, S. H. Lee, M. Jeong, J. H. Shin, Y. Ahn, D. Kim, S. H. Oh, S. H. Yun, ACS Nano 2018, 12, 9587.

[94] K. I. Park, M. Lee, Y. Liu, S. Moon, G. T. Hwang, G. Zhu, J. E. Kim, S. O. Kim, D. K. Kim, Z. L. Wang, Adv. Mater. 2012, 24, 2999.

[95] V. Marinov, O. Swenson, R. Miller, F. Sarwar, Y. Atanasov, M. Semler, S. Datta, IEEE Trans. Compon., Packag., Manuf. Technol. 2011, 2, 569.

[96] J. H. Park, H. E. Lee, C. K. Jeong, S. K. Hong, K.-I. Park, K. J. Lee, Nano Energy 2019, 56, 531.

[97] T. Kobayashi, M. Haruta, K. Sasagawa, M. Matsumata, K. Eizumi, C. Kitsumoto, M. Motoyama, Y. Maezawa, Y. Ohta, T. Noda, Sci. Rep. 2016, 6, 21247.

[98] H. E. Lee, J. H. Shin, S. H. Lee, J. H. Lee, S. H. Park, K. J. Lee, Flexible Micro Light-emitting Diodes for Wearable Applications, Light-Emitting Devices, Materials, and Applications, International Society for Optics and Photonics, Bellingham, WA, USA 2019, pp. 109400F.

[99] C. Lu, S. Park, T. J. Richner, A. Derry, I. Brown, C. Hou, S. Rao, J. Kang, C. T. Moritz, Y. Fink, Sci. Adv. 2017, 3, e1600955.

[100] H. Lee, M. Kim, I. Kim, Adv. Mater. 2016, 28, 4541.[101] H. E. Lee, J. Choi, S. H. Lee, M. Jeong, J. H. Shin, D. J. Joe, D. Kim,

C. W. Kim, J. H. Park, J. H. Lee, Adv. Mater. 2018, 30, 1800649.[102] P. Rajasethupathy, E. Ferenczi, K. Deisseroth, Cell 2016, 165, 524.[103] Y. Zhang, A. D. Mickle, P. Gutruf, L. A. McIlvried, H. Guo, Y. Wu,

J. P. Golden, Y. Xue, J. G. Grajales-Reyes, X. Wang, Sci. Adv. 2019, 5, eaaw5296.

[104] J. Sudimack, R. J. Lee, Adv. Drug Delivery Rev. 2000, 41, 147.[105] M. S. Lesniak, H. Brem, Nat. Rev. Drug Discovery 2004, 3, 499.[106] A. M. Asiri, A. Mohammad, Woodhead Publishing, Sawston,

Cambridge, UK 2018.[107] A. Lecomte, V. Castagnola, E. Descamps, L. Dahan, M.-C. Blatché,

T. Dinis, E. Leclerc, C. Egles, C. Bergaud, J. Micromech. Microeng. 2015, 25, 125003.

[108] A. Lecomte, V. Castagnola, E. Descamps, L. Dahan, M.-C. Blatché, T. Dinis, E. Leclerc, C. Egles, C. Bergaud, J. Micromech. Microeng. 2015, 25, 125003.

[109] H. Lee, T. K. Choi, Y. B. Lee, H. R. Cho, R. Ghaffari, L. Wang, H. J. Choi, T. D. Chung, N. Lu, T. Hyeon, Nat. Nanotechnol. 2016, 11, 566.

[110] K. N. Noh, S. I. Park, R. Qazi, Z. Zou, A. D. Mickle, J. G. Grajales-Reyes, K. I. Jang, R. W. Gereau IV, J. Xiao, J. A. Rogers, Small 2018, 14, 1702479.

[111] J.-W. Jeong, J. G. McCall, G. Shin, Y. Zhang, R. Al-Hasani, M. Kim, S. Li, J. Y. Sim, K.-I. Jang, Y. Shi, Cell 2015, 162, 662.

[112] R. Farra, N. F. Sheppard, L. McCabe, R. M. Neer, J. M. Anderson, J. T. Santini, M. J. Cima, R. Langer, Sci. Transl. Med. 2012, 4, 122ra21.



Adv. Mater. 2020, 32, 1907522

[113] J. T. Santini Jr., M. J. Cima, Nature 1999, 397, 335.[114] J. Marchand-Brynaert, M. Deldime, I. Dupont, J.-L. Dewez,

Y.-J. Schneider, J. Colloid Interface Sci. 1995, 173, 236.[115] H. Matsuno, A. Yokoyama, F. Watari, M. Uo, T. Kawasaki, Biomate-

rials 2001, 22, 1253.[116] B. Merchant, Biologicals 1998, 26, 49.[117] K. V. Nemani, K. L. Moodie, J. B. Brennick, A. Su, B. Gimi, Mater.

Sci. Eng., C 2013, 33, 4453.[118] K. Park, Y. H. Lim, M. Jang, A. Kim, H.-J. Kim, S. H. Paek, B. Jeon,

Stereot. Funct. Neurosurg. 2018, 96, 65.[119] R. de Haas, R. Struikmans, G. van der Plasse, L. van Kerkhof,

J. H. Brakkee, M. J. Kas, H. G. Westenberg, J. Neurosci. Methods 2012, 209, 113.

[120] Z. Xiang, J. Liu, Microsyst. Nanoeng. 2016, 2, 16012.[121] X. Wang, J. Song, J. Liu, Z. L. Wang, Science 2007, 316, 102.[122] Z. Li, G. Zhu, R. Yang, A. C. Wang, Z. L. Wang, Adv. Mater. 2010,

22, 2534.[123] G. T. Hwang, H. Park, J. H. Lee, S. Oh, K. I. Park, M. Byun, H. Park,

G. Ahn, C. K. Jeong, K. No, Adv. Mater. 2014, 26, 4880.[124] C. K. Jeong, I. Kim, K.-I. Park, M. H. Oh, H. Paik, G.-T. Hwang,

K. No, Y. S. Nam, K. J. Lee, ACS Nano 2013, 7, 11016.[125] K. I. Park, C. K. Jeong, J. Ryu, G. T. Hwang, K. J. Lee, Adv. Energy

Mater. 2013, 3, 1539.[126] K. I. Park, J. H. Son, G. T. Hwang, C. K. Jeong, J. Ryu, M. Koo,

I. Choi, S. H. Lee, M. Byun, Z. L. Wang, Adv. Mater. 2014, 26, 2514.[127] J. J. Vidal, Annu. Rev. Biophys. Bioeng. 1973, 2, 157.[128] Y. Chae, J. Jeong, S. Jo, IEEE Trans. Robot. 2012, 28, 1131.[129] M. Bryan, J. Green, M. Chung, L. Chang, R. Scherer, J. Smith,

R. P. Rao, in 2011 11th IEEE-RAS International Conference on Humanoid Robots, IEEE, Piscataway, NJ, USA 2011, p. 199.

[130] B. Choi, S. Jo, PLoS One 2013, 8, e74583.

[131] O. Cohen, S. Druon, S. Lengagne, A. Mendelsohn, R. Malach, A. Kheddar, D. Friedman, in 2012 4th IEEE RAS & EMBS Inter-national Conference on Biomedical Robotics and Biomechatronics (BioRob), IEEE, Piscataway, NJ, USA 2012, p. 314.

[132] S.-S. Yoo, H. Kim, E. Filandrianos, S. J. Taghados, S. Park, PLoS One 2013, 8, e60410.

[133] J. J. Vidal, Annu. Rev. Biophys. Bioeng. 1973, 2, 157.[134] J. Minguillon, M. A. Lopez-Gordo, F. Pelayo, Biomed. Signal

Process. Control 2017, 31, 407.[135] N. Weiskopf, K. Mathiak, S. W. Bock, F. Scharnowski, R. Veit,

W. Grodd, R. Goebel, N. Birbaumer, IEEE Trans. Biomed. Eng. 2004, 51, 966.

[136] S. D. Power, A. Kushki, T. Chau, BMC Res. Notes 2012, 5, 141.[137] R. C. Wykes, D. M. Kullmann, I. Pavlov, J. Neurosci. Methods 2016,

260, 215.[138] A. V. Kravitz, B. S. Freeze, P. R. Parker, K. Kay, M. T. Thwin,

K. Deisseroth, A. C. Kreitzer, Nature 2010, 466, 622.[139] J. Kim, Y. Kim, R. Nakajima, A. Shin, M. Jeong, A. H. Park, Y. Jeong,

S. Jo, S. Yang, H. Park, S. H. Cho, K. H. Cho, I. Shim, J. H. Chung, S. B. Paik, G. J. Augustine, D. Kim, Neuron 2017, 95, 1181.

[140] K. M. Tye, J. J. Mirzabekov, M. R. Warden, E. A. Ferenczi, H.-C. Tsai, J. Finkelstein, S.-Y. Kim, A. Adhikari, K. R. Thompson, A. S. Andalman, Nature 2013, 493, 537.

[141] J. H. Stockley, K. Evans, M. Matthey, K. Volbracht, S. Agathou, J. Mukanowa, J. Burrone, R. T. Karadottir, Sci. Rep. 2017, 7, 849.

[142] E. Musk, J. Med. Internet Res. 2019, 21, e16194.[143] S. Reardon, Nat. News 2016, 530, 264.[144] P. Foggia, A. Saggese, N. Strisciuglio, M. Vento, in 11th IEEE Int.

Conf. on Advanced Video and Signal Based Surveillance (AVSS), IEEE, Piscataway, NJ, USA 2014.

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