Smart Parking Systems have emerged as a transformative solution to address the growing challenges associated with urbanization and increasing vehicular traffic. Such system integrates sensors, cameras, and other IoT connected devices to monitor parking spaces in real time. However, there are many security vulnerabilities in existing solutions, especially when it comes to car authentication at parking entry points. IoT sensors my be susceptible to Cyber-attacks and fraudulent activities, such as car theft, can exploit these vulnerabilities due to limited built-in security features. The reliability of authentication systems, based on IoT sensors can also be compromised by factors such as extreme weather conditions and physical damage. The cyber-physical solution we propose relies on Physical Unclonable Functions (PUFs) for identification and authentication in IoT devices to mitigate these challenges. The use of PUFs enhances the reliability and security of smart parking systems against unauthorized access and fraud. Furthermore, to ensure the integrity and confidentiality of the data within the smart parking ecosystem and to improve authentication process, we propose the implementation of a tailored blockchain framework. This framework incorporates lightweight local blockchains dedicated to individual parking slots, complemented by a central blockchain that manages data at the city level. The experimental results demonstrate the feasibility of the PUF computation process, showcasing an acceptable runtime for practical implementation. In the experimental results, we evaluated the SRAM used for the PUF implementation process and demonstrated its stability (intra HD equals to 2.25.
Open access
Physical Unclonable Functions (PUFs) and Hardware Security
Akshay Kulkarni, Noor Ahmad Hazari, Mohammed Niamat
Globalization of integrated circuits (ICs) may lead to the quality of ICs being compromised due to the possible untrusted entities involved in the supply chain. There have been well documented cases of chips secretly implanted with Trojans creeping into the supply chain. Studies have shown that tampering lithographic masks, also called as reticles, is one of the potential sources of hardware Trojan intrusion. This paper presents a novel blockchain-enabled mask writing technique to combat the alteration of the IC layout design at the mask making step. A blockchain-enabled file storage and transfer system for secure transfer of the layout GDSII file from the design house to the mask making machine is studied in this work. As part of this investigation, a smart contract is developed, which interacts with an external application programming interface (API) and fetches the design layout file from the file storage system. The smart contract developed as part of the research can be adopted in the existing EDA tools for mask making, curtailing the access sought by an adversary in the mask making process. The proposed smart contract is developed using Solidity language, on an online IDE called Remix. Finally, a case study is presented in this paper, validating the approach and simulating the proposed smart contract. The simulation of the smart contract is conducted on Goerli test network provided by Ethereum.
Physical Unclonable Functions (PUFs) and Hardware Security
Integrated Circuits and Semiconductor Failure Analysis
Tao Zhang, Fahim Rahman, Mark Tehranipoor, Farimah Farahmandi
Field-programmable gate array (FPGA) bitstream reverse engineering and counterfeiting is a pertinent challenge in the modern hardware supply chain. To this end, this article proposes a blockchain-based technology to foster authenticity and integrity of the FPGA supply chain for trustworthy traceability. The proposed approach is transformative in being able to detect counterfeit FPGA chips and bitstreams using state-of-the-art blockchain technologies.āKanad Basu, The University of Texas at Dallas
Physical Unclonable Functions (PUFs) and Hardware Security
Integrated Circuits and Semiconductor Failure Analysis
Venkata K. V. V. Bathalapalli, Saraju P. Mohanty, Elias Kougianos, Babu Kaji Baniya Ā· 5 authors
This article presents the first-ever hardware-assisted blockchain for simultaneously handling device and data security in smart healthcare. This article presents the hardware security primitive physical unclonable functions (PUF) and blockchain technology together as PUFchain 2.0 with a two-level authentication mechanism. The proposed PUFchain 2.0 security primitive presents a scalable approach by allowing Internet of Medical Things (IoMT) devices to connect and obtain PUF keys from the edge server with an embedded PUF module instead of connecting a PUF module to each device. The PUF key, once assigned to a particular media access control (MAC) address by the miner, will be unique for that MAC address and cannot be assigned to other devices. PUFs are developed based on internal micro-manufacturing process variations during chip fabrication. This property of PUFs is integrated with blockchain by including the PUF key of the IoMT into blockchain for authentication. The robustness of the proposed Proof of PUF-Enabled authentication consensus mechanism in PUFchain 2.0 has been substantiated through test bed evaluation. Arbiter PUFs have been used for the experimental validation of PUFchain 2.0. From the obtained 200 PUF keys, 75% are reliable and the Hamming distance of the PUF module is 48%. Obtained database outputs along with other metrics have been presented for validating the potential of PUFchain 2.0 in smart healthcare.
Open access
Physical Unclonable Functions (PUFs) and Hardware Security
At present, Blockchain is one of the biggest sought after research-oriented field that finds application in numerous areas including finance, cryptocurrency, asset management and so on. Blockchain is basically a public distributed database that holds the encrypted data. To ensure that each transaction is true and correct, the blocks are initially validated and then agreed upon by the user through a consensus mechanism like Proof of Work (PoW), Proof of Stake (PoS) etc [57] [53]. The most commonly used consensus mechanism over the year is PoW, which involves the user to solve a computationally hard puzzle. But, the problem with this traditional consensus mechanism is that the whole process is power and resource hungry and requires a considerable amount of time for the computation. This huge requirement can be reduced considerably by replacing these algorithms with certain hardware primitives without even compromising on the correctness, security and trust features. This project work focuses on providing a viable replacement to the authentication/ validation and mining part of the existing Consensus algorithm. This work make use of a Ring Oscillator PUF (ROPUF) for authenticating the identity of user and a reconfigurable LFSR for doing the mining function. This is a research attempt to study on how hardware primitives can be used as a substitute to the power and resource hungry mechanisms currently in use. This aims at reducing the burden on hefty software by leveraging the advantages of cost effective hardware primitives.
Physical Unclonable Functions (PUFs) and Hardware Security
As a result of the increasingly pervasive deployment of the Internet of Things(IoT), the cybersecurity of IoT has already attracted more and more research efforts. Identity management is believed to be the fundamental keystone to build security mechanisms. The traditional centralized identity management scheme suffers from a single point of failure and identity forgery. A secure IoT system framework was proposed leveraging blockchain as the basic infrastructure with Physical Unclonable Functions (PUFs) identifying the sensors uniquely. We brought up a scheme to improve the identity authentication protocol. Experiments showed that our approach was more effective and secure against attacks.
Open access
Physical Unclonable Functions (PUFs) and Hardware Security
In this paper I report the discovery of neurons which showed a neural correlate with ongoing fluctuations of Bitcoin and Ethereum prices at the time of the recording. I used the publicly available dataset of Neuropixel recordings by the Allen Institute to correlate the firing rate of single neurons with cryptocurrency price. Out of ~40.000 recorded single neurons, ~70% showed a significant correlation with Bitcoin or Ethereum prices. Even when using the conservative Bonferroni correction for multiple comparisons, ~35% of neurons showed a significant correlation, which is well above the expected false positive rate of 5%. These results were due to "nonsense correlations": when correlating two signals which both evolve slowly over time, the chances of finding a significant correlation between the two are much higher than when comparing signals which lack this property.
In the blockchain, the transaction hashes are implemented through public-key cryptography and hash functions. Hence, there is a possibility for the two users to choose the same private key knowingly or unknowingly. Even the intruders can follow the particular user's bitcoin transaction, and they can masquerade as that user by generating the private and public key pairs of him. If it happens, the user may lose his transaction. Generally, bitcoin technology uses random numbers from 1 to 2256. It is a wide range, but for a greater number of users, there should be one another solution. There is a possibility of digital prototyping which leads to the loss of more accounts. This chapter provides the device-specific fingerprint technology known as physical unclonable function (PUF) to be employed for authentication in a blockchain-based bitcoin environment. The random unique response from PUF ensures correct transaction. In this chapter, a new tetrahedral oscillator PUF has been introduced intrinsically. All the blockchain operations are carried out and verified with PUF response.
Open access
Physical Unclonable Functions (PUFs) and Hardware Security
A detailed review on the technological aspects of Blockchain and Physical Unclonable Functions (PUFs) is presented in this article. It stipulates an emerging concept of Blockchain that integrates hardware security primitives via PUFs to solve bandwidth, integration, scalability, latency, and energy requirements for the Internet-of-Energy (IoE) systems. This hybrid approach, hereinafter termed as PUFChain, provides device and data provenance which records data origins, history of data generation and processing, and clone-proof device identification and authentication, thus possible to track the sources and reasons of any cyber attack. In addition to this, we review the key areas of design, development, and implementation, which will give us the insight on seamless integration with legacy IoE systems, reliability, cyber resilience, and future research challenges.
Open access
Physical Unclonable Functions (PUFs) and Hardware Security
Sina Rafati Niya, Benjamin Jeffrey, Burkhard Stiller
The integration of Internet-of-Things (IoT) and Blockchains (BC) for trusted and decentralized approaches enabled modern use cases, such as supply chain tracing, smart cities, and IoT data marketplaces. For these it is essential to identify reliably IoT devices, since the producer-consumer trust is not guaranteed by a Trusted Third Party (TTP). Therefore, this work proposes a Know Your IoT device platform (KYoT), which enables the self-sovereign identification of IoT devices on the Ethereum BC. KYoT permits manufacturers and device owners to register and verify IoT devices in a self-sovereign fashion, while data storage security is ensured. KYoT deploys an SRAM-based (Static Random Access Memory) Physically Unclonable Function (PUF), which takes advantage of the manufacturing variability of devices' SRAM chips to derive a unique identifying key for each IoT device. The self-sovereign identification mechanism introduced is based on the ERC 734 and ERC 735 Ethereum identity standards.
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Physical Unclonable Functions (PUFs) and Hardware Security
Blockchain technology is a game-changing, enhancing security for the supply chain of smart additive manufacturing. Blockchain enables the tracking and recording of the history of each transaction in a ledger stored in the cloud that cannot be altered, and when blockchain is combined with digital signatures, it verifies the identity of the participants with its non-repudiation capabilities. One of the weaknesses of blockchain is the difficulty of preventing malicious participants from gaining access to publicāprivate key pairs. Groups of opponents often interact freely with the network, and this is a security concern when cloud-based methods manage the key pairs. Therefore, we are proposing end-to-end security schemes by both inserting tamper-resistant devices in the hardware of the peripheral devices and using ternary cryptography. The tamper-resistant devices, which are designed with nanomaterials, act as Physical Unclonable Functions to generate secret cryptographic keys. One-time use publicāprivate key pairs are generated for each transaction. In addition, the cryptographic scheme incorporates a third logic state to mitigate man-in-the-middle attacks. The generation of these publicāprivate key pairs is compatible with post quantum cryptography. The third scheme we are proposing is the use of noise injection techniques used with high-performance computing to increase the security of the system. We present prototypes to demonstrate the feasibility of these schemes and to quantify the relevant parameters. We conclude by presenting the value of blockchains to secure the logistics of additive manufacturing operations.
Open access
Physical Unclonable Functions (PUFs) and Hardware Security
The present era is witnessing a reuse of hardware IPs to reduce cost. As trustworthiness is an essential factor, designers prefer to use hardware IPs which performed effectively in the past, but at the same time, are still active and did not age. In such scenarios, pay per use licensing schemes suit best for both producers and users. Existing pay per use licensing mechanisms consider a centralized third party, which may not be trustworthy. Hence, we seek refuge to blockchain technology to eradicate such third parties and facilitate a transparent and automated pay per use licensing mechanism. A blockchain is a distributed public ledger whose records are added based on peer review and majority consensus of its participants, that cannot be tampered or modified later. Smart contracts are deployed to facilitate the mechanism. Even dynamic pricing of the hardware IPs based on the factors of trustworthiness and aging have been focused in this work, which are not associated in existing literature. Security analysis of the proposed mechanism has been provided. Performance evaluation is carried based on the gas usage of Ethereum Solidity test environment, along with cost analysis based on lifetime and related user ratings.
Physical Unclonable Functions (PUFs) and Hardware Security
Among all the different research lines related to hardware security, there is a particular topic that strikingly attracts attention. That topic is the research regarding the so-called Physical Unclonable Functions (PUF). The PUFs, as can be seen throughout the Thesis, present the novel idea of connecting digital values uniquely to a physical entity, just as human biometrics does, but with electronic devices. This beautiful idea is not free of obstacles, and is the core of this Thesis. It is studied from different angles in order to better understand, in particular, SRAM PUFs, and to be able to integrate them into complex systems that expand their potential. During Chapter 1, the PUFs, their properties and their main characteristics are defined. In addition, the different types of PUFs, and their main applications in the field of security are also summarized. Once we know what a PUF is, and the types of them we can find, throughout Chapter 2 an exhaustive analysis of the SRAM PUFs is carried out, given the wide availability of SRAMs today in most electronic circuits (which dramatically reduces the cost of deploying any solution). An algorithm is proposed to improve the characteristics of SRAM PUFs, both to generate identifiers and to generate random numbers, simultaneously. The results of this Chapter demonstrates the feasibility of implementing the algorithm, so in the following Chapters it is explored its integration in both hardware and software systems. In Chapter 3 the hardware design and integration of the algorithm introduced in Chapter 2 is described. The design is presented together with some examples of use that demonstrate the possible practical realizations in VLSI designs. In an analogous way, in Chapter 4 the software design and integration of the algorithm introduced in Chapter 2 is described. The design is presented together with some examples of use that demonstrate the possible practical realizations in low-power IoT devices. The algorithm is also described as part of a secure firmware update protocol that has been designed to be resistant to most current attacks, ensuring the integrity and trustworthiness of the updated firmware.In Chapter 5, following the integration of PUF-based solutions into protocols, PUFs are used as part of an authentication protocol that uses zero-knowledge proofs. The cryptographic protocol is a Lattice-based post-quantum protocol that guarantees the integrity and anonymity of the identity generated by the PUF. This type of architecture prevents any type of impersonation or virtual copy of the PUF, since this is unknown and never leaves the device. Specifically, this type of design has been carried out with the aim of having traceability of identities without ever knowing the identity behind, which is very interesting for blockchain technologies. Finally, in Chapter 6 a new type of PUF, named as BPUF (Behavioral and Physical Unclonable Function), is proposed and analyzed according to the definitions given in Chapter 1. This new type of PUF significantly changes the metrics and concepts to which we were used to in previous Chapters. A new multi-modal authentication protocol is presented in this Chapter, taking advantage of the challenge-response tuples of BPUFs. An example of BPUFs is illustrated with SRAMs. A proposal to integrate the BPUFs described in Chapter 6 into the protocol of Chapter 5, as well as the final remarks of the Thesis, can be found in Chapter 7.
Physical Unclonable Functions (PUFs) and Hardware Security
Integrated Circuits and Semiconductor Failure Analysis
A key challenge of the embedded era is to ensure trust in reuse of intellectual properties (IPs), which facilitates reduction of design cost and meeting of stringent marketing deadlines. Determining source of the IPs or their authenticity is a key metric to facilitate safe reuse of IPs. Though physical unclonable functions solves this problem for application specific integrated circuit (ASIC) IPs, authentication strategies for reconfigurable IPs (RIPs) or IPs of reconfigurable hardware platforms like field programmable gate arrays (FPGAs) are still in their infancy. Existing authentication techniques for RIPs that relies on verification of proof of authentication (PoA) mark embedded in the RIP by the RIP producers, leak useful clues about the PoA mark. This results in replication and implantation of the PoA mark in fake RIPs. This not only causes loss to authorized second hand RIP users, but also poses risk to the reputation of the RIP producers. We propose a zero knowledge authentication strategy for safe reusing of RIPs. The PoA of an RIP producer is kept secret and verification is carried out based on traversal times from the initial point to several intermediate points of the embedded PoA when the RIPs configure an FPGA. Such delays are user specific and cannot be replicated as these depend on intrinsic properties of the base semiconductor material of the FPGA, which is unique and never same as that of another FPGA. Experimental results validate our proposed mechanism. High strength even for low overhead ISCAS benchmarks, considered as PoA for experimentation depict the prospects of our proposed methodology.
Physical Unclonable Functions (PUFs) and Hardware Security
Neuroscience and Neural Engineering
Integrated Circuits and Semiconductor Failure Analysis
The cost to develop a new integrated circuit (IC), its fabrication, debug and volume production has been escalating with scaling of transistor feature size. According to an IBS report, the cost of developing a System on Chip (SoC) at 14nm may be as high as $300 million [1]. The economics of semiconductor IC development favors high volume production, while high volume cannot be attained without developing an IC that serves a large number of applications. Some of these applications are in low margin Internet of Things (IoT) devices, where an SoC cannot command a high price. Consequently, without the ability to customize IC features after production, the price of an IC will be determined by its lowest priced application. This motivates the manufacturers to develop capabilities for post-production IC customization. The commodity microprocessor business offers an example of post-production customization, where a manufacturer can tailor cache size, number of cores and frequency of operation for a target market segment after a chip has been manufactured. Today, such customization is limited to one-time programming (OTP) for predetermined IC bins. In this paper, we explore how an IC can be programmed repeatedly and securely using a blockchain-based smart contract. This will enable users to upgrade IC features, or rent upgraded IC features for a fixed period after it has been purchased. The availability of such a system could, for example, allow a buyer to upgrade her processor from i3 to i5 after it has been purchased to scale to her computing needs in exchange of a payment made to the manufacturer. IC feature configuration is implemented by firmware updates from the manufacturer. The smart contract takes the feature configuration request from the IC as input and outputs the source of corresponding firmware. To support remote and authorized update by manufacturer, we propose an on-die hardware module that communicates with the smart contract and enforces its functionalities. Availability of this module also facilitates secure firmware update. The blockchain makes the update protocol secure and prevents users from obtaining unauthorized update.
Physical Unclonable Functions (PUFs) and Hardware Security
Electronic systems are ubiquitous today, playing an irreplaceable role in our personal lives as well as in critical infrastructures such as power grid, satellite communication, and public transportation. In the past few decades, the security of software running on these systems has received significant attention. However, hardware has been assumed to be trustworthy and reliable "by default" without really analyzing the vulnerabilities in the electronics supply chain. With the rapid globalization of the semiconductor industry, it has become challenging to ensure the integrity and security of hardware. In this paper, we discuss the integrity concerns associated with a globalized electronics supply chain. More specifically, we divide the supply chain into six distinct entities: IP owner/foundry (OCM), distributor, assembler, integrator, end user, and electronics recycler, and analyze the vulnerabilities and threats associated with each stage. To address the concerns of the supply chain integrity, we propose a blockchain-based certificate authority framework that can be used to manage critical chip information such as electronic chip identification (ECID), chip grade, transaction time, etc. The decentralized nature of the proposed framework can mitigate most threats of the electronics supply chain, such as recycling, remarking, cloning, and overproduction.
Open access
Physical Unclonable Functions (PUFs) and Hardware Security
Neuroscience and Neural Engineering
Integrated Circuits and Semiconductor Failure Analysis
With ubiquitous adoption of connected sensors, actuators and smart devices are finding inroads into daily life. Internet of Things (IoT) authentication is rapidly transforming from classical cryptographic user-centric knowledge based approaches to device signature based automated methodologies to corroborate identity between claimant and a verifier. Physical Unclonable Function (PUF) based IoT authentication mechanisms are gaining widespread interest as users are required to access IoT devices in real time while also expecting execution of sensitive (even physical) IoT actions immediately. This paper, delineates combination of BlockChain and Sensor based PUF authentication mechanism for solving real-time but non-repudiable access to IoT devices in a Smart Home by utilizing a mining less consensus mechanism for the provision of immutable assurance to users' and IoT devices' transactions i.e. commands, status alerts, actions etc.
Physical Unclonable Functions (PUFs) and Hardware Security
Jul 1, 2018Ā·2018 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData)
Blockchain technology has brought a huge paradigm shift in multiple industries, by integrating distributed ledger, smart contracts and consensus protocol under the same roof. Notable applications of blockchain include cryptocurrencies and large-scale multi-party transaction management systems. The latter fits very well into the domain of manufacturing and supply chain management for Integrated Circuits (IC), which, despite several advanced technologies, is vulnerable to malicious practices, such as overproduction, IP piracy and deleterious design modification to gain unfair advantages. To combat these threats, researchers have proposed several ideas like hardware metering, design obfuscation, split manufacturing and watermarking. In this paper, we show, how these issues can be complementarily dealt with using blockchain technology coupled with identity-based encryption and physical unclonable functions, for improved resilience against certain adversarial motives. As part of our proposed blockchain protocol, titled `BLIC', we propose an authentication mechanism to secure both active and passive IC transactions, and a composite consensus protocol designed for IC supply chains. We also present studies on the security, scalability, privacy and anonymity of the BLIC protocol.
Physical Unclonable Functions (PUFs) and Hardware Security
Traceability of ICs is important for verifying provenance. We present a novel IC traceability scheme based on blockchain. A blockchain is an immutable public record that maintains a continuously-growing list of data records secured from tampering and revision. In the proposed scheme, IC ownership transfer information is recorded and archived in a blockchain. This safe, verifiable method prevents any party from altering or challenging the legitimacy of the information being exchanged. However, we also need to establish correspondence between a record in a public database and the physical device, in an unclonable way. We propose an embedded physically unclonable function (PUF) to establish this correspondence The blockchain ensures the legitimacy of an IC's current owner, while the PUF safeguards against IC counterfeiting and tampering. Our proposed solution combines hardware and software protocols to let supply chain participants authenticate, track, trace, analyze, and provision chips during their entire life cycle.
Physical Unclonable Functions (PUFs) and Hardware Security
Integrated Circuits and Semiconductor Failure Analysis
Timothy H. Lucas, Xilin Liu, Milin Zhang, Sri Sritharan Ā· 10 authors
BCI: brainācomputer interface DCN: dorsal column nuclei ICMS: intracortical microstimulation LED: light-emitting diode PDMS: polydimethylsiloxane RF: radiofrequency The dexterous hand is a defining feature of human existence. Evolved over tens of millions of years, modern humans are able to perform remarkable tasks with their hands. From typing hundreds of words per minute to playing Rachmaninoff's Piano Concerto No. 2, the dexterous hand defines us. Unfortunately, a number of maladies rob us of this defining human characteristic. In the most extreme case, paralyzed individuals lose communication between the brain and the periphery. This condition affects an estimated 5.4 million people, or 2% of the US population.1 At present, no effective treatment restores function to these individuals. Regaining hand function is a principal concern for paralyzed patients. Toward this aim, significant advances in motorāor efferentābrainācomputer interface (BCI) systems have occurred in recent years. Efferent BCI systems extract movement-relevant information from electrocorticography (ECoG) or electroencephalography (EEG). These analogue signals are transformed into control commands to drive robotic arms2 or evoke muscle contractions in paralyzed limbs.3-8 In the later example, compound wrist flexion may be evoked by brain-controlled functional electrical stimulation of forearm flexors. Planned clinical trials aim to capitalize upon these scientific advances to test efferent BCI across a range of conditions and control routines. While these proof-of-principal systems are encouraging, a number of substantial hurdles remain. Perhaps the most pressing barrier to restoring dexterous hand movements is the lack of systems to restore somatosensory feedback. Even in the presence of intact descending motor systems, precise hand movements are abolished when somatosensation is missing.9-16 Indeed, the majority of efferent BCI systems currently in testing rely solely upon visual guidance. This constraint is unnatural and unlikely to be useful if deployed clinically. Visual guidance requires constant vigilance and introduces substantial time-lags to error correct each movement. To restore naturalistic movements, bi-directional BCI systems that link movements and real-time sensory feedback must be developed. The feedback loop of bi-directional BCI is closed with sensory feedback. Unfortunately, the field of sensoryāor afferentābrainācomputer interface has not kept pace with the maturation of efferent systems. This is due, in part, to the challenges concerning sensory research in animals. Sensory perception is a uniquely subjective experience that does not lend itself readily to the quantitative metrics. For decades, experimentalists have attempted to characterize the perceptual experiences associated with stimulation of the sensory cortices, including primary somatosensory cortex (S1), secondary somatosensory cortex (S2), and parietal association areas in animal models. From this body of literature, we know that intracortical microstimulation (ICMS) of S1 yields sufficient percepts to permit limited binary decisions, such as differentiating between 2 stimulation frequencies or amplitudes.17-21 Despite exhaustive investigation, no study has convincingly reproduced the complex sensory phenomena that are fundamental to our routine encounters with the physical world. Compounding the problem, very limited human data are available to assess the efficacy of S1 stimulation. Animal studies do not answer the question of how stimulation feels. To answer these qualitative questions, we need human data. Most human data have been obtained during brief testing sessions in awake craniotomies or during stimulation in patients with implanted ECoG electrodes.22-24 Invariably, these patients reported that S1 stimulation yielded only vague ātinglingā sensations with modest regional localization. Flesher and colleagues recently reported the first human data using ICMS encoding in S1 with chronic penetrating arrays.25 In this experiment, a 28-yr-old male with a spinal cord injury underwent implantation of 2 32-channel multi-electrode arrays into primary somatosensory cortex (S1). Over the course of several months, the investigators mapped perceptual responses to ICMS up to 100 Ī¼A. The majority of responses (93%) were categorized as āpossibly natural,ā āpressureā sensations. The perceptual intensity was modulated by stimulation amplitude with increased pressure corresponding to increase stimulus amplitude. This finding mirrors that of ICMS in primary visual cortex where phosphine brightness is modulated by stimulus amplitude.26 These data constitute a substantial step toward clinical sensory BCI. However, there were a number of findings that tempered enthusiasm for immediately clinical implementation. For instance, none of the S1 electrodes activated sensory representations of the distal fingers where feedback is most needed. Instead, the majority of responses were localized to the palmar crease region of the hand proximal to the fingers. Also, the detection thresholds of a many electrode sites rose significantly over the short course of the study, raising the concern that the effect of S1 encoding will fade over time. Finally, few of the stimuli evoked properly ānaturalisticā percepts. These limitations and the disappointing results from similar work in visual cortex raise the question of whether cortical ICMS encoding is the optimal solution for sensory restoration. These unanswered questions motivate our research program. Our work aims to bridge the divide between current state-of-the-art and the clinical needs of our patients. Our overarching strategy is to develop closed-loop, autonomous bidirectional braināmachine interface systems. These systems, as conceived, provide real-time communication between the brain and body. Because the field of efferent BCI has vastly outpaced that of afferent BCI, our work primarily focuses on developing sensory-brain interfaces to couple with existing BCIs (see Bouton et al27 for example). Our strategy focuses on 3 critical intersections of engineering and neuroscience. The first is development of a suite of sensors that serve as mechanoreceptors for the paralyzed, insensate hand. The second is development of a chronic neural interface for artificial sensory encoding. The third is a body area network that links peripheral sensors with novel neural interfaces. The integration of these components is illustrated in Figure 1.FIGURE 1: Body area network. Fully integrated system with implantable force and flex sensors (1, 2), wearable analyzer (3), electrogoniometer (4), and neural interface (5).In this brief overview, we outline our approach, preliminary data, and future directions. This work collectively represents a fruitful collaboration between neurosurgery and electrical engineering. We are grateful to the National Science Foundation for funding our work. RESEARCH APPROACH Our research strategy follows 3 central aims: development of novel sensors, characterization of novel neural interfaces, and development of an autonomous body-area network. Novel Sensors Hand somatosensation can be characterized by a multidimensional space with axes defined by sensory modality (eg, light touch, proprioception), somatotopy, temporal dynamics, the influence of descending central inputs, and brain state. Restoring native somatosensation is perhaps too lofty a goal for a first-generation sensorābrain interface. Instead, we reduce the dimensionality of the problem to a single sensory modality at a single somatotopic location. We have developed a number of force sensors and a proprioceptive sensor as our first aim. The design of our force sensors is constrained by the form and function of the human hand. Relevant design features include: sensor sensitivity, range, power, form-factor, and complexity. Sensitivity is defined as a sensor's accuracy to convert mechanical force into voltage changes on the sensor. Dynamic range captures the extremes of mechanical force spanning interactions between the hand and the physical environment. The feature of power concerns both the requirements of the sensor (active or passive) as well as the sensor's efficiency to convert physical energy into electrical energy. For wireless sensors, the power feature also includes power harvesting and wireless transmission of data. Form-factor is defined as the mechanical properties of the sensor (size, shape) as well as the flexibility and elasticity of the substrate. Finally, the complexity of the sensor constrains fabrication and durability. These competing design constraints inevitably require engineering trade-offs. In the interest of brevity, we focus on 2 prototype force sensors and a proprioceptive sensor to illustrate these engineering trade-offs in the context of sensorābrain interface. First we consider scattering force sensors and optical force sensors before moving toward proprioceptive electrogoniometers. Scattering force sensors operate under the principle of radiofrequency (RF) back scatter. RF identification is a common technique used to track tags, like those attached to garments at a department store to prevent theft or those implanted subdermally in house pets to identify them when they are lost. The central concept is that RF energy polarizes conductive elements, such as the linear segments of an antenna, and scatter energy back in a measurable way. Deformations of the segment length or shape cause a shift in the back-scatter pattern as the polarization of each segment is related to its orientation in a pulsed electromagnetic field. By calibrating the back-scatter patterns induced by force-induced deformations of RF antenna segments, one may indirectly measure forces applied to a flexible antenna implanted under the skin. In the first series of experiments, our group characterized the back-scatter signatures of a number of antenna designs serving as passive sensor nodes. An advantage of passive sensors is that they do not require active power supplies. Therefore, flexible antennas can be implanted under the skin without the need of wires or batteries. Initial antennas were made with copper tape for rapid prototyping. Antenna shapes were constructed into space filling curves (eg, Hilbert, Peano curves) that varied in the number and length of conductive segments (Figure 2). Changes in size and shape of copper RF antennas were associated with reproducible batter scatter properties.FIGURE 2: Passive scattering force sensor design. A, Antenna shapes with different linear segments in second order Hilbert and Peano curves. B and C, Polarization of antenna segments within electromagnetic field. D, Radiofrequency response curves as a function of area of RF tag (left), and shifts in curves with Ā±2% change in area (right). E, Prototype indiumāgallium tags in PDMS substrate. Central reservoir visible in series with antenna segments. F, RF tuning curve of indiumāgallium tags in response to forces applied to central reservoir. Rapid shift noted in low end of force axes indicates appropriate sensitivity for precise finger grip.To build force sensitivity, our second series of experiments examined the flexibility of antennas across a range of forces routinely encountered by the human hand. Liquid metal indiumāgallium antennas were designed within a flexible, skin-like polydimethylsiloxane (PDMS) substrate. Indiumāgallium is a highly conductive eutectic alloy whose melting point is sufficiently low (ā¼ ā2Ā°F) to allow the alloy to remain in liquid phase at room temperature. Channels were laser-etched into the PDMS in the shape of space filling curves to house the alloy (Figure 2). Force sensitivity was amplified by creating a central compressible metal reservoir in series with the channels. When force was applied, the liquid metal filled the channel segments proportionally. As each successful segment of the antenna was filled with conductive metal, the RF back-scatter properties shifted (Figure 2). As can be seen in the RF response curve, the antenna was sufficiently sensitive to capture force changes within 5 N of fingertip pressure, appropriate for precision grip activities. These experiments verified the feasibility of force sensing RF tags. However, limitations to this technique include the need for sensitive detecting antennas to measure back scatter. For this reason, we examined force sensor designs that were independent of RF signal. Optical force sensing is a method to detect fingertip pressure without electromagnetic interference. An optical force sensor layers PDMS membrane on SiO2 within an implantable chip (Figure 3) that could be implanted subdermally. At one end of the floor of the sensor, an internal 80 Ī¼m2 light-emitting diode (LED) emits light. The light is reflected by the internal ceiling of the chip that is constructed of PDMS in an inverse lenticular structure. Reflected light is detected by a photodiode at the opposite end of the sensor. The intervening SiO2 acts as an optical waveguide. In the absence of force (or compressing pressure), the waveguide allows reflected light to excite the photodiode with an efficient electric-to-optical conversion, a high sensitivity (0.02 kPaā1) and a pressure sensing resolution (38 mPa). When force is applied, the PDMS ceiling bows downward, opening light channels in the membrane. This allows light to escape, which in turn decreases the voltage at the photodiode monotonically, and yields a scaled readout.FIGURE 3: Optical force sensor design. A, Side view of optical sensor in absence of load. Directional path of light shown in yellow reflected from internal surface of PDMS ceiling. LED emitter located in lower left of sensor; photodiode (PD) located in lower right. B, Applied forces reduce light received by photodiode end. C, Diagram of optical force sensor circuit. D, Idealized relationship between applied force and photodiode voltageBoth scatter sensors and optical sensors achieved their desired engineering goals of converting force into measurable data. Neither system represented optimal solutions. In the case of scatter sensors, environmental noise may obscure the back-scatter energy detected by a horn antenna. In the case of optical sensors, an active circuit is required. On-going experiments aim to address these limitations by increasing the signal-to-noise ratio (RF sensors) and integrating rechargeable power (optical sensors). Beyond touch sensation, proprioception is a fundamental sensory modality that informs us about limb position. To restore proprioception across large joints, we developed a wireless electrogoniometer.28 Unlike other electrogoniometers that require strain gauges or power-hungry potentiometers, our system was designed to have very low power requirements (ā¼20 Ī¼W) both in terms of sensing and wireless data transmission. This was achieved using a pair of impulse-radio ultrawide band wireless smart sensor nodes interfacing with low-power 3-axis accelerometers through event-driven analog-to-digital converters. Electrogoniometers are designed to operate across large joints, such as the elbow, which are too large for strain sensors or other position sensors. On-going experiments aim to combine multiple sensor modalities in the same organism. Novel Central Nervous System Targets Our second aim is to identify optimal sensory encoding nodes along the neuraxis. Cortical encoding has been attempted for decades in animals, and recently in humans, with mixed results. It remains to be seen how well S1 ICMS will faithfully reproduce naturalistic perception. ICMS in other sensory areas, like primary visual cortex, generates phosphenes but not complex visual images.26 This may be due to the fact that cortical representations are distributed. Complex experiential phenomena, like rich somatosensory percepts, are therefore unlikely to be reproduced with focal stimulation without activation of a larger network. Upstream sensory circuits have To to this we developed the first chronic neural interface of the dorsal column nuclei to and stimulation in awake The a for sensory encoding. These nuclei on the dorsal surface of the and proprioceptive signals from primary (Figure from the high information to the for sensory the descending from that may sensory column nuclei interface. A, between and nuclei and in are readily with of the B, implanted in the of a at of in for several C, of of electrode in to studies of the were limited to or In our first of in were implanted with multi-electrode arrays to the feasibility of a chronic interface. Over several months, we that these arrays are and well in without data from implanted yielded a number of Over were The most was that over that are frequencies occurred with a in the we that could be over multiple in with chronic by the results of we designed a series of stimulation In experiments, we the at sensory encoding through at in a highly precise stimulation of the evoked responses in primary sensory stimulation evoked and field in the S1 (Figure which is to from sensory The induced for up to This finding may the of perceptual experiences primary that circuits between and S1 have a function for sensory To test perceptual thresholds of were on a detection and When stimuli were with to detect the electrical stimuli over rose to thresholds for are to cortical thresholds This that encoding experiments to characterize the efficacy of evoked Cortical responses to encoding. A, evoked responses to stimulation. to the which B, of frequencies stimulation. the stimulus at a well the stimulus feasibility of and encoding testing not from these experiments have for somatosensory currently will characterize responses and their to nodes including the and sensory Novel BCI Novel systems are to link peripheral sensor nodes and sensory encoding We developed a bidirectional braināmachine the as our third aim. This when links a suite of implantable and wearable peripheral sensor nodes with neural and electrodes (Figure the system and its nodes are to as a body area network. At the of the are wireless including a neural a neural a sensor and a The of a neural neural feature neural and associated The neural feature are for or field the system includes an neural energy and a detection with control is in the form of a that sensor data from peripheral sensors to desired patterns related to somatosensory cortex (Figure of brainācomputer interface A, intersections between BCI systems and in the case of paralyzed or feedback control from nodes within in B, control loop integrating neural and stimulation in the flexibility to paralyzed or neural may be to or or stimulation with a voltage of Our the to current the that that neural and current current stimulation with a to a phase that neural However, changes due to during the Over millions of develop and in that the interface and To properly for this we a feedback that the phase when a point is detected (Figure The of this circuit is an error that error by the during stimulus the are the range, stimulation are as error that this method over that the system will have in experiments are to test this principle A, and of between phase and phase shown Idealized shown in shown in B, the on of point body area network requires real-time communication between the is with an impulse-radio band The and components to and between For clinical communication between nodes must be and operate within an of data The features an data of 2 in The error was over a of 3 of these are well within the desired for human moving toward human a number of must be of the system must be in must also be to To whether of the system was and effective at percepts, we designed in experiments on the the were to a to the by visual a pattern the is were to ICMS by the as a on the (Figure As the animal the stimulation As significantly in the presence of perception. When the was during was are able to systems to perception in a and effective In feasibility testing of novel systems. A, B, design is to in by ICMS as Idealized illustrated from with and optimal in presence of In our strategy to develop a sensorābrain interface system focuses on 3 aims: development of novel sensors, characterization of novel neural and development of autonomous body-area network. We have made in each of these areas, but substantial work We to our systems up to channel peripheral sensors and our In we our systems to animal models. It is our goal to this sensor brain interface with existing efferent systems to a bidirectional BCI to paralyzed This work was in by the National Science The have no or interest in of the or in this