This special collection explores the rapidly evolving field of wearable and implantable electronic devices. These integrated microsystems leverage state-of-the-art technologies in electrical, magnetic, optical, and ultrasound neuromodulation and recording to interact with biological tissues (Hu et al. 2024). Designed to be soft, flexible, stretchable, biocompatible, and minimally invasive, these devices enable long-term implantation (Yifei et al. 2024). They are engineered for adaptability, featuring self-learning capabilities and the ability to adjust and upgrade themselves to meet changing therapeutic needs, ultimately improving patient outcomes. Furthermore, continuous operation of these devices is enabled through the integration of wireless power transfer, physiological energy harvesting, multiplexed signal acquisition, local signal processing, and wireless data transmission, facilitating the development of soft, multimodal, and scalable flexible electronic systems for reliable long-term biointerfacing (Won et al. 2018, De Ridder et al. 2024). Recent breakthroughs in materials science and microfabrication have facilitated the seamless integration of bioelectronic devices with the human body. Emerging technologies such as electronic skin, neural stimulation electrodes, and optogenetics have demonstrated significant potential in the diagnosis and treatment of neurological diseases, inflammatory conditions, and other complex disorders, improving treatment precision and effectiveness (Wu et al. 2023, Liu et al. 2024a). These innovations not only advance the development of wearable and implantable devices but also provide solid technical support for smart healthcare and precision medicine. They are expected to have a profound and lasting impact on the future of medical care. [...]

Optical wireless power transfer (OWPT) has emerged as a promising technology for efficient wireless power transfer (WPT), offering advantages, such as directionality, suitability for far-field applications, and the ability to transfer power and data simultaneously. This comprehensive review classifies OWPT systems into laser power transfer (LPT) and light-emitting diodes (LED)-based OWPT. LPT uses the narrow divergence of laser beams for high-density, long-distance energy transfer, making it suitable for applications, such as satellites, autonomous drones, and electric vehicle charging. In contrast, LED-based OWPT offers a safer, more cost-effective solution for low-power applications, especially in the Internet of Things (IoT) domain. It offers advantages, such as lower power consumption and fewer safety restrictions compared to LPT. Innovations in LPT, such as high-intensity laser power beaming, distributed laser charging (DLC), adaptive DLC, simultaneous lightwave information and power transfer, and resonant beam charging are discussed. Also, recent advancements in LED-OWPT, including single-lens and double-lens systems, collimation techniques, and multi-LED arrays, are explored for their potential in powering IoT devices, wearable electronics, and smart infrastructure. First, we present a radar chart comparing various WPT techniques with respect to performance criteria. After reviewing the methods of LPT and LED-OWPT in detail, a comparison of these techniques is provided, evaluating their strengths, limitations, and application suitability. A concluding radar chart offers insights for optimizing OWPT systems tailored to specific applications. Future research directions are identified, emphasizing the need for further advancements in beam alignment, safety protocols, and hybrid systems to enhance OWPT’s scalability and practicality in real-world scenarios.

Silicon integrated circuits (ICs) are central to the next-generation miniature active neural implants, whether packaged in soft polymers for flexible bioelectronics or implanted as bare die for neural probes. These emerging applications bring the IC closer to the corrosive body environment, raising reliability concerns, particularly for chronic use. Here, we evaluate the inherent hermeticity of bare die ICs, and examine the potential of polydimethylsiloxane (PDMS), a moisture-permeable elastomer, as a standalone encapsulation material. For this aim, the electrical and material performance of ICs sourced from two foundries was evaluated through one-year accelerated in vitro and in vivo studies. ICs featured custom-designed test structures and were partially PDMS coated, creating two regions on each chip, uncoated “bare die” and “PDMS-coated”. During the accelerated in vitro study, ICs were electrically biased and periodically monitored. Results revealed stable electrical performance, indicating the unaffected operation of ICs even when directly exposed to physiological fluids. Despite this, material analysis revealed IC degradation in the bare regions. PDMS-coated regions, however, revealed limited degradation, making PDMS a suitable IC encapsulant for years-long implantation. Based on the new insights, guidelines are proposed that may enhance the longevity of implantable ICs, broadening their applications in the biomedical field.

Miniaturization of next-generation active neural implants requires novel micro-packaging solutions that can maintain their long-term coating performance in the body. This work presents two thin-film coatings and evaluates their biostability and in vivo performance over a 7-month animal study. To evaluate the coatings on representative surfaces, two silicon microchips with different surface microtopography are used. Microchips are coated with either a ≈100 nm thick inorganic hafnium-based multilayer deposited via atomic layer deposition (ALD-ML), or a ≈6 µm thick hybrid organic–inorganic Parylene C and titanium-based ALD multilayer stack (ParC-ALD-ML). After 7 months of direct exposure to the body environment, the multilayer coatings are evaluated using optical and cross-sectional scanning electron microscopy. Time-of-flight secondary ion mass spectrometry (ToF-SIMS) is also used to evaluate the chemical stability and barrier performance of the layers after long-term exposure to body media. Results showed the excellent biostability of the 100 nm ALD-ML coating with no ionic penetration within the layer. For the ParC-ALD-ML, concurrent surface degradation and ion ingress are detected within the top ≈70 nm of the outer Parylene C layer. The results and evaluation techniques presented here can enable future material selection, packaging, and analysis, enhancing the functional stability of future chip-embedded neural implants.

Correction to: Nature Communicationshttps://doi.org/10.1038/s41467-024-55298-4, published online 02 January 2025 In this article the following sentence was omitted from the acknowledgements section, ‘This research was funded by the following projects: Project CANDO (Controlling Network Dynamics with Optogenetics), funded by UK EPSRC (grant ref: NS/A000026/1) and the Wellcome Trust (contract ref: 102037/Z/13/Z)’. The original article has been corrected.

State-of-the-art intracortical neural recording and stimulation systems rely on subdural implants tethered to a cranial implant which itself has a wireless power and data link to the outside world [1] (Fig. 6.2.1). However, this tethered configuration poses challenges such as scarring and potential damage to the surrounding tissue due to strain and micromotions, making this approach unsuitable for chronical implants [2]. Consequently, there is growing interest in wireless connections between cranial and subdural implants. This paper focuses on wireless powering between implants, traversing the dura and cerebrospinal fluid (CSF) tissue layers over distances of 0.5 to 1cm (transdural powering). With modern burr-hole craniotomy, the hole drilled in the skull is 6mm in diameter, limiting the available size for the TX. Moreover, the power dissipation of the TX must be low to keep tissue heating below 1°C [3]. RF and optical modalities suffer from higher attenuation in tissue compared to ultrasound (0.6dB/cm/MHz) [4]. Furthermore, for transdural powering, power losses from reflections at medium interfaces (e.g., skull) are avoided, making ultrasound (US) a prime candidate for efficient in-body wireless power transfer. US is also preferable to inductive powering since US beam steering up to large angles (>45°) is needed to maximize power delivery and compensate for brain micromotions of up to ±4mm [5] and misalignment during surgery. However, prior art US driving systems either use single-phase transducer driving [6, 7], incapable of beam steering, or use class D drivers with low power transfer efficiency (PTE) [8, 9]. A phased array with increased driving efficiency was presented in [10], but it cannot perform beam steering without grating lobes that can be eliminated with miniature transducers with a pitch close to λ/2. To facilitate direct integration between CMOS and the transducer array, the CMOS driving units should also be pitch matched [8, 9].

Objectives: Current techniques in brain stimulation are still largely based on a phrenologic approach that a single brain target can treat a brain disorder. Nevertheless, meta-analyses of brain implants indicate an overall success rate of 50% improvement in 50% of patients, irrespective of the brain-related disorder. Thus, there is still a large margin for improvement. The goal of this manuscript is to 1) develop a general theoretical framework of brain functioning that is amenable to surgical neuromodulation, and 2) describe the engineering requirements of the next generation of implantable brain stimulators that follow from this theoretic model. Materials and Methods: A neuroscience and engineering literature review was performed to develop a universal theoretical model of brain functioning and dysfunctioning amenable to surgical neuromodulation. Results: Even though a single target can modulate an entire network, research in network science reveals that many brain disorders are the consequence of maladaptive interactions among multiple networks rather than a single network. Consequently, targeting the main connector hubs of those multiple interacting networks involved in a brain disorder is theoretically more beneficial. We, thus, envision next-generation network implants that will rely on distributed, multisite neuromodulation targeting correlated and anticorrelated interacting brain networks, juxtaposing alternative implant configurations, and finally providing solid recommendations for the realization of such implants. In doing so, this study pinpoints the potential shortcomings of other similar efforts in the field, which somehow fall short of the requirements. Conclusion: The concept of network stimulation holds great promise as a universal approach for treating neurologic and psychiatric disorders.

In this paper, we present the surface modification of multilayer graphene electrodes with platinum (Pt) nanoparticles (NPs) using spark ablation. This method yields an individually selective local printing of NPs on an electrode surface at room temperature in a dry process. NP printing is performed as a post-process step to enhance the electrochemical characteristics of graphene electrodes. The NP-printed electrode shows significant improvements in impedance, charge storage capacity (CSC), and charge injection capacity (CIC), versus the equivalent electrodes without NPs. Specifically, electrodes with 40% NP surface density demonstrate 4.5 times lower impedance, 15 times higher CSC, and 4 times better CIC. Electrochemical stability, assessed via continuous cyclic voltammetry (CV) and voltage transient (VT) tests, indicated minimal deviations from the initial performance, while mechanical stability, assessed via ultrasonic vibration, is also improved after the NP printing. Importantly, NP surface densities up to 40% maintain the electrode optical transparency required for compatibility with optical imaging and optogenetics. These results demonstrate selective NP deposition and local modification of electrochemical properties in graphene electrodes for the first time, enabling the cohabitation of graphene electrodes with different electrochemical and optical characteristics on the same substrate for neural interfacing.

Recording neuronal activity triggered by electrical impulses is a powerful tool in neuroscience research and neural engineering. It is often applied in acute electrophysiological experimental settings to record compound nerve action potentials. However, the elicited neural response is often distorted by electrical stimulus artifacts, complicating subsequent analysis. In this work, we present a model to better understand the effect of the selected amplifier configuration and the location of the ground electrode in a practical electrophysiological nerve setup. Simulation results show that the stimulus artifact can be reduced by more than an order of magnitude if the placement of the ground electrode, its impedance, and the amplifier configuration are optimized. We experimentally demonstrate the effects in three different settings, in-vivo and in-vitro.

Electrical stimulation is proven to be an effective way of neuromodulation in bioelectronic medicine (e.g. cochlear implants, deep brain stimulators, etc.), delivering localized treatment by the means of electrical pulses. To increase the stimulation efficiency and neural-type selectivity, there is an increasing interest to employ non-rectangular stimulation waveforms [1-4]. Even though delivering and storing digital data at the stimulator provides the highest flexibility for generating stimulation waveforms, state-of-the-art approaches suffer either from poor resolution or the requirement of high data bandwidth for wirelessly powered implants [2]. Using Analog waveform generators is an alternative approach at the cost of extra implementation complexity for each type of waveform [3]. To fulfill the same goals as employing arbitrary waveforms for stimulation, we propose to shape the typical rectangular waveform using a programmable first-order low-pass filter, mimicking the natural filtering characteristic of the neural membrane. Using bio-realistic modeling, we show that such a pre-filtered waveform requires less or equal energy for the activation of neurons when compared with other energy-efficient waveforms (e.g. Gaussian). Notably, this comes at the low cost of only one extra programmable parameter (i.e., the filter’s corner frequency), on top of the typical duration and amplitude parameters. The basic concept of this work is driven by the fact that the natural low-pass characteristic of the neuron’s membrane limits the energy transfer efficiency from the stimulator to the cell. Thus, it is proposed to pre-filter the high-frequency components of the stimulus [4]. The method is validated for a Hodgkin-Huxley (HH) axon-cable model using NEURON v8.0 software. The required activation energy is simulated for rectangular, Gaussian, half-sine, triangular, ramp-up, and ramp-down waveforms, all with pulse durations of 10-1000µs, and low-pass filtered with cut-off frequencies of 0.5-50kHz. Simulations show a 51.5% reduction in the required activation energy for the shortest rectangular pulse (i.e., 10-μs pulse width) after filtering at 5kHz. It is also shown that the minimum required activation energy can be decreased by 11.04%, 9.49%, 8.28%, 1.81%, 0.17%, and 0% when an appropriate pre-filter is applied to the rectangular, ramp-down, ramp-up, half-sine, triangular, and Gaussian waveforms, respectively. Finally, a perspective usage of this method to improve the selectivity of electrical stimulation is drawn.

Developing neuroprosthetic bioelectronic devices requires wirelessly-powered implantable stimulator systems with hundreds to thousands of output channels. Power efficiency optimization is crucial for scaling up the number of output channels. Current-mode electrical stimulation is favored for safety but is power-inefficient in conventional designs, particularly in multichannel stimulators. An adaptive voltage supply can improve power efficiency, but implementing channel-specific voltage supplies in large-scale systems is challenging. Conventional power management suffers from losses and low efficiency due to multiple conversion stages. This work proposes a multichannel current-mode stimulator with a parallel, adaptive ac/dc power management strategy using single-stage phase-controlled converters to prevent cascaded losses. This allows for generating channel-specific supply voltages within a small area for high power efficiency and high-density electrical stimulation. The proposed circuit was designed and simulated using TSMC 180 nm technology and demonstrates an improvement in the power efficiency of up to 45% with respect to a conventional power-management strategy using a fixed supply voltage.

This paper presents a new communication method between micro-scale freely floating brain implants based on galvanic coupling (GC), called "Brain-Coupled Communication" (BCC). Since the transmission efficiency based on GC is highly dependent on the system’s geometry and the electromagnetic properties of the tissue, finite element models in COMSOL Multiphysics® are employed for characterizing the proposed method. Concurrent scaling of channel length (i.e., the distance between two implants), the inter-electrode distance (on a single implant), and electrode dimensions with a constant ratio down to 2 % of their typical values show an increase in the optimum frequency of the communication by 50 times (from 200 kHz to 10 MHz). This, in turn, yields a substantial increase in the channel bandwidth. The proposed method also shows excellent robustness against misalignment. Up to 60 ° of angular misalignment and 1 mm of lateral displacement result in a voltage-gain attenuation of less than 5 dB and 2 dB, respectively. Furthermore, a negligible shading effect between implants is observed by exploring multi-implant scenarios. Moreover, based on the conducted compliance study, no safety hazards were observed for the intended conditions. In conclusion, the proposed method exhibits a multitude of desirable qualities that position it as an excellent choice for establishing a network of freely floating brain implants.

The key challenges in designing a multi-channel biosignal acquisition system for an ambulatory or invasive medical application with a high channel count are reducing the power consumption, area consumption and the outgoing wire count. This article proposes a spread-spectrum modulated biosignal acquisition system using a shared amplifier and an analog-to-digital converter (ADC). We propose a design method to optimize a recording system for a given application based on the required SNR performance, number of inputs, and area. The proposed method is tested and validated on real pre-recorded atrial electrograms and achieves an average percentage root-mean-square difference (PRD) performance of 2.65% and 3.02% for sinus rhythm (SR) and atrial fibrillation (AF), respectively by using pseudo-random binary-sequence (PRBS) codes with a code-length of 511, for 16 inputs. We implement a 4-input spread-spectrum analog front-end in a 0.18 μ m CMOS process to demonstrate the proposed approach. The analog front-end consists of a shared amplifier, a 2nd order Σ Δ ADC sampled at 7.8 MHz, used for digitization, and an on-chip 7-bit PRBS generator. It achieves a number-of-inputs to outgoing-wire ratio of 4:1 while consuming 23 μ A/input including biasing from a 1.8 V power supply and 0.067 mm ² in area.

This paper presents a novel multi-channel stimulation backend with a multi-bit delta-sigma control loop, which enables precise adjustment of the stimulation current through modulation of the supply voltage. This minimizes the overhead voltage of series circuitry to the stimulation load and avoids the associated energy loss. Additionally, to address the bandwidth limitations commonly encountered in battery-less implants, we propose incorporating amplitude and duration scaling of the arbitrary stimulation waveform. The waveform is programmable with 64 7-bit samples and 4 scaling factors per channel, resulting in a minimum of 68% data reduction per channel compared to using the waveform without scaling. The proposed circuits are designed and simulated in 180nm BCD technology occupying a total silicon area of 9mm2. The fully integrated backend has a minimum compliance voltage of 8.5V and features a switched-capacitor multi-output DC-DC converter (MODDC) with pulse-skipping capability, a CMOS-only high-voltage (HV) multiplexer, and a unique HV H-bridge. Programming a sine-wave stimulus with a 4mA amplitude and a duration of 256μs achieved a signal-to-noise ratio of 40dB within a 10kHz bandwidth. For the same waveform, power efficiencies of 94% and 68% were observed without and with MODDC, respectively. Additionally, when programming constant-current stimuli ranging from 0.26mA to 4mA, high efficiencies of 78-97% and 23-79.4% were achieved without and with MODDC, respectively.

In an attempt to reduce the side effects caused by the chemically-based drugs used to treat neurological disorders, the field of bioelectronics has been focusing on the development of smart and reliable solutions that could, ideally, interact with the tissue at a resolution of individual cells.1 Conventionally, electrically-based systems have been used.2 However, increasing the resolution at which they interact with the body leads to the development of invasive electrode arrays, which can cause long-term side effects.3 Another approach, based on acoustic waves, has recently emerged. Ultrasound (US) neuromodulation has been proven to be effective in modulating the response of peripheral nerves, in an in-vivo setup4 and has the potential to achieve higher spatial selectivity.5 In this work, we aim to fabricate an implantable cuff for US neuromodulation, which would employ an array of US transducers to deliver focused US to specific nerve areas in a non-invasive manner. To this end, the potential of different US transducer arrays for peripheral nerve applications is evaluated, assessing the acoustic performances as well as ease of assembly and integration. More specifically, two of the most important parameters that affect neural excitation are the frequency and output pressure generated by the US transducers.4 Conventional bulk PZT transducers can generate a wide range of output pressures but these are not small enough for this application. PZT-based arrays, integrated on CMOS have recently emerged, and will be part of this evaluation6. However, these have not yet been integrated on flexible substrates. On the other hand, micromachined US transducers (MUTs) have been gaining a lot of interest, particularly capacitive MUTs (CMUTs) which can operate at high frequencies, thus reducing the focal point significantly.5 CMUTs can be fabricated on flexible substrates, using biocompatible materials, rendering them a very attractive candidate for the envisioned cuff. However, CMUTs usually feature lower Q factors compared to PZTs, hence the output pressure still has to be evaluated for neuromodulation. In addition, this work will also discuss important characteristics of the materials used for encapsulation, as these should ensure the required flexibility of the cuff without negatively affecting the acoustic performance of the transducers.

During the last few decades, electrical neural stimulators have successfully been employed as a means of treatment for a wide range of neurological disorders. By targeting the peripheral and central nervous systems, electrical neurostimulators activate/inhibit neural activity by manipulating the stimulus- induced electric field arising at the targeted area through diverse electrode configurations. Aiming at reducing the overall size of implanted stimulator systems, these are being designed to be wirelessly powered and batteryless. Technologies for wireless power transfer to implants are mainly based on inductive coupling and, more recently, ultrasonic waves.
To increase the power efficiency and thereby minimize the power consumption of the implant, we have previously proposed a stimulation technique that alters the electric field at the tissue by delivering charge in small packets in a very rapid manner (e.g. 1 Mpps). This charge is consequently accumulated by the tissue’s integrating nature.1 This approach removes the need to ensure continuous accurate control of the stimulus current amplitude, resulting in power savings. To the same end, in an ultrasonically powered system, unnecessary power-conversion blocks can be eliminated, as the incoming ultrasonic wave is harvested, converted into an electrical signal, rectified, and then directly used for stimulation.2
Inspired by the above, this work focuses on providing a discrete-component multi-channel neural stimulator, powered wirelessly through ultrasound (US), to activate and inhibit neural activity. The use of mostly commercially available discrete components will ensure reproducibility by other research labs and help provide a platform technology that can be used as an experimental tool in a variety of applications.3 The US pressure wave obtained at the receiving US transducer will be converted into electrical energy and used both for powering the system as well as to shape the charge packets of the eventual stimulus pulse. In this manner, the need for DC-DC up-conversion, typically needed for neuromodulation, will be eliminated.4 A charge-balancing technique, matching the fast pulse repetition rates (PRR) required for inhibition of neural activity (typically ≥ 5 kHz), will ensure the safety of the implant, while the injected charge will be constantly monitored and adapted for safe and efficacious activation/inhibition.

This work proposes a guideline for designing more energy-efficient electrical stimulators by analyzing the frequency spectrum of the stimuli. It is shown that the natural low-pass characteristic of the neuron’s membrane limits the energy transfer efficiency from the stimulator to the cell. Thus, to improve the transfer efficiency, it is proposed to pre-filter the high-frequency components of the stimulus. The method is validated for a Hodgkin-Huxley (HH) axon cable model using NEURON v8.0 software. To this end, the required activation energy is simulated for rectangular pulses with durations between 10 µs and 5 ms, which are low-pass filtered with cut-off frequencies of 0.5-50 kHz. Simulations show a 51.5% reduction in the required activation energy for the shortest pulse width (i.e., 10 µs) after filtering at 5 kHz. It is also shown that the minimum required activation energy can be decreased by 11.04% when an appropriate pre-filter is applied. Finally, we draw a perspective for future use of this method to improve the selectivity of electrical stimulation.

A Medical Body Area Network (MBAN) is an ensemble of collaborating, potentially heterogeneous, medical devices located inside, on the surface of or around the human body with the objective of tackling one or multiple medical conditions of the MBAN host. These devices-which are a special category of Wireless Body Area Networks (WBANs)–collect, process and transfer medical data outside of the network, while in some cases they also administer medical treatment autonomously. Since communication is so pivotal to their operation, the newfangled IEEE 802.15.6 standard is aimed at the communication aspects of WBANs. It places a set of physical and communication constraints while it also includes association/disassociation protocols and security services that WBAN applications need to comply with. However, the security specifications put forward by the standard can be easily shown to be insufficient when considering realistic MBAN use cases and need further enhancements. The present work addresses these shortcomings by, first, providing a structured analysis of the IEEE 802.15.6 security features and, afterwards, proposing comprehensive and tangible recommendations on improving the standard’s security.

Power efficiency in electrical stimulator circuits is crucial for developing large-scale multichannel applications like bidirectional brain-computer interfaces and neuroprosthetic devices. Many state-of-the-art papers have suggested that some non-rectangular pulse shapes are more energy-efficient for exciting neural excitation than the conventional rectangular shape. However, additional losses in the stimulator circuit, which arise from employing such pulses, were not considered. In this work, we analyze the total energy efficiency of a stimulation system featuring non-rectangular stimuli, taking into account the losses in the stimulator circuit. To this end, activation current thresholds for different pulse shapes and durations in cortical neurons are modeled, and the energy required to generate the pulses from a constant voltage supply is calculated. The proposed calculation reveals an energy increase of 14%–51% for non-rectangular pulses compared to the conventional rectangular stimuli, instead of the decrease claimed in previous literature. This result indicates that a rectangular stimulation pulse is more power-efficient than the tested alternative shapes in large-scale multichannel electrical stimulation systems.

In multi-electrode arrays (MEAs), for electrical recording and electrical stimulation, high voltage (HV) switches are employed to build an analog multiplexer to conduct either a HV stimulation signal from the pulse generator circuit to an electrode or a low-voltage noise-sensitive signal from the electrode to a recording stage. In this brief, a new circuit structure is proposed for HV switches that significantly improves their switching speed, reduces their power dissipation, and also employs a minimum number of bulky HV devices. In order to reduce the transition time of the proposed switch without consuming a large amount of power, during the transition time that the switch is turning on/off, a large current flows through the driver circuit of the switch to turn the switch on/off rapidly; however, after the transition times, this current is significantly reduced to save power. Based on the proposed HV switch, a multiplexer structure has been implemented in 25-V, 0.18- μm CMOS IC technology and the measurement results prove its efficacy. Supplied with 25 V and operating with 300 nA current consumption, this switch swings over 20 V with a relatively constant on-resistance of 100Ω and features a 80 ns transition time.