Closed-Loop Control of Functional Electrical Stimulation Using a Selectively Recording and Bidirectional Nerve Cuff Interface.

Discriminating recorded afferent neural information can provide sensory feedback for closed-loop control of functional electrical stimulation, which restores movement to paralyzed limbs. Previous work achieved state-of-the-art off-line classification of electrical activity in different neural pathways recorded by a multi-contact nerve cuff electrode, by applying deep learning to spatiotemporal neural patterns. The objective of this study was to demonstrate the feasibility of this approach in the context of closed-loop stimulation. Acute in vivo experiments were conducted on 11 Long Evans rats to demonstrate closed-loop stimulation. A 64-channel ( 8×8 ) nerve cuff electrode was implanted on each rat's sciatic nerve for recording and stimulation. A convolutional neural network (CNN) was trained with spatiotemporal signal recordings associated with 3 different states of the hindpaw (dorsiflexion, plantarflexion, and pricking of the heel). After training, firing rates were reconstructed from the classifier outputs for each of the three target classes. A rule-based closed-loop controller was implemented to produce ankle movement trajectories using neural stimulation, based on the classified nerve recordings. Closed-loop stimulation was successfully demonstrated in 6 subjects. The number of successful movement sequence trials per subject ranged from 1-17 and number of correct state transitions per trial ranged from 3-53. This work demonstrates that a CNN applied to multi-contact nerve cuff recordings can be used for closed-loop control of functional electrical stimulation.

Resource-Efficient Neural Network Architectures for Classifying Nerve Cuff Recordings on Implantable Devices.

BACKGROUND: Closed-loop functional electrical stimulation can use recorded nerve signals to create implantable systems that make decisions regarding nerve stimulation in real-time. Previous work demonstrated convolutional neural network (CNN) discrimination of activity from different neural pathways recorded by a high-density multi-contact nerve cuff electrode, achieving state-of-the-art performance but requiring too much data storage and power for a practical implementation on surgically implanted hardware. OBJECTIVE: To reduce resource utilization for an implantable implementation, with minimal performance loss for CNNs that can discriminate between neural pathways in multi-contact cuff electrode recordings. METHODS: Neural networks (NNs) were evaluated using rat sciatic nerve recordings previously collected using 56-channel cuff electrodes to capture spatiotemporal neural activity patterns. NNs were trained to classify individual, natural compound action potentials (nCAPs) elicited by sensory stimuli. Three architectures were explored: the previously reported ESCAPE-NET, a fully convolutional network, and a recurrent neural network. Variations of each architecture were evaluated based on F1-score, number of weights, and floating-point operations (FLOPs). RESULTS: NNs were identified that, when compared to ESCAPE-NET, require 1,132-1,787x fewer weights, 389-995x less memory, and 6-11,073x fewer FLOPs, while maintaining macro F1-scores of 0.70-0.71 compared to a baseline of 0.75. Memory requirements range from 22.69 KB to 58.11 KB, falling within on-chip memory sizes from published deep learning accelerators fabricated in ASIC technology. CONCLUSION: Reduced versions of ESCAPE-NET require significantly fewer resources without significant accuracy loss, thus can be more easily incorporated into a surgically implantable device that performs closed-loop responsive neural stimulation.

Hardware-Efficient 1D CNN for Patient-Specific Early Seizure Detection.

Closed-loop brain-implantable neuromodulation devices are a new treatment option for patients with refractory epilepsy. Seizure detection algorithms implemented on such devices are subject to strict power and area constraints. Deep learning methods, though very powerful, tend to have high computational complexity and thus are typically impractical for resource-constrained neuromodulation devices. In this paper, we propose a compact and hardware-efficient one-dimensional convolutional neural network (1D CNN) structure for patient-specific early seizure detection. Feature extraction techniques and a novel initialization method based on the forward-chaining training and testing scheme are used to improve model performance. Our compact model achieves similar accuracy to that of support vector machines, the state-of-the-art method for seizure detection, while consuming over 20x less power.

Fascicle-Selective Ultrasound-Powered Bidirectional Wireless Peripheral Nerve Interface IC.

This paper presents an innovative, minimally invasive, battery-free, wireless, peripheral nervous system (PNS) neural interface, which seamlessly integrates a millimeter-scale, fascicle-selective integrated circuit (IC) with extraneural recording and stimulating channels. The system also incorporates a wearable interrogator equipped with integrated machine-learning capabilities. This PNS interface is specifically tailored for adaptive neuromodulation therapy, targeting individuals with paralysis, amputation, or chronic medical conditions. By employing a neural pathway classifier and temporal interference stimulation, the proposed interface achieves precise deep fascicle selectivity for recording and stimulation without the need for nerve penetration or compression. Ultrasonic energy harvesters facilitate wireless power harvesting and data reception, enhancing the usability of the system. Key circuit performance metrics encompass a 2.2 µVrms input-referred noise, 14-bit ENOB, and a 173 dB Schreier figure of merit (FOM) for the neural analog-to-digital converter (ADC). Additionally, the ultra-low-power radio-frequency (RF) transmitter boasts a remarkable 1.38 pJ/bit energy efficiency. In vivo experiments conducted on rat sciatic nerves provide compelling evidence of the interface's ability to selectively stimulate and record neural fascicles. The proposed PNS neural interface offers alternative treatment options for diagnosing and treating neurological disorders, as well as restoring or repairing neural functions, improving the quality of life for patients with neurological and sensory deficits.

Spike sorting algorithms and their efficient hardware implementation: a comprehensive survey.

Objective. Spike sorting is a set of techniques used to analyze extracellular neural recordings, attributing individual spikes to individual neurons. This field has gained significant interest in neuroscience due to advances in implantable microelectrode arrays, capable of recording thousands of neurons simultaneously. High-density electrodes, combined with efficient and accurate spike sorting systems, are essential for various applications, including brain machine interfaces (BMIs), experimental neural prosthetics, real-time neurological disorder monitoring, and neuroscience research. However, given the resource constraints of modern applications, relying solely on algorithmic innovation is not enough. Instead, a co-optimization approach that combines hardware and spike sorting algorithms must be taken to develop neural recording systems suitable for resource-constrained environments, such as wearable devices and BMIs. This co-design requires careful consideration when selecting appropriate spike-sorting algorithms that match specific hardware and use cases.Approach. We investigated the recent literature on spike sorting, both in terms of hardware advancements and algorithms innovations. Moreover, we dedicated special attention to identifying suitable algorithm-hardware combinations, and their respective real-world applicabilities.Main results. In this review, we first examined the current progress in algorithms, and described the recent departure from the conventional '3-step' algorithms in favor of more advanced template matching or machine-learning-based techniques. Next, we explored innovative hardware options, including application-specific integrated circuits, field-programmable gate arrays, and in-memory computing devices (IMCs). Additionally, the challenges and future opportunities for spike sorting are discussed.Significance. This comprehensive review systematically summarizes the latest spike sorting techniques and demonstrates how they enable researchers to overcome traditional obstacles and unlock novel applications. Our goal is for this work to serve as a roadmap for future researchers seeking to identify the most appropriate spike sorting implementations for various experimental settings. By doing so, we aim to facilitate the advancement of this exciting field and promote the development of innovative solutions that drive progress in neural engineering research.

Adaptively Clock-Boosted Auto-Ranging Neural-Interface for Emerging Neuromodulation Applications.

Responsive deep brain stimulation (DBS) requires recruiting deep brain structures without affecting the superficial neuronal population. Neurosurgeons widely use implanted electrodes, which are highly localized but invasive, to stimulate the deep brain. Temporally interfering stimulation (TIS) excites the deep brain non-invasively. This neuromodulation technique utilizes two high-frequency sinusoidal electric fields that do not recruit superficial neural structures but have a small frequency differential. The small differential causes a low-frequency interference envelope that stimulates deep regions and is steerable by changing the intensity of the electric fields without physically moving the electrodes. Using TIS as a non-invasive DBS method generates high-frequency stimulation artifacts at recording sites, which may saturate a conventional recording front-end. This paper presents a low-power bidirectional 64-channel CMOS neural-ADC that is immune to artifacts such as those in the TIS techniques or conventional biphasic stimulation. The presented DC-coupled chopped analog front-end leverages delta-spectrum shaping to remove electrode DC offset voltage and maintain the input impedance higher than 250 MΩ, which is sufficient for interfacing with non-invasive scalp electrodes. The AFE operates on the input signal difference to detect large and rapid stimulation artifacts. It incorporates both exponential tracking and boosted-rate sampling to recover within 100 µs. Upon recovery, the neural-ADC range and speed are reduced to achieve noise and power efficiency factors of 2.98 and 10.6, respectively. In vivo recordings from anesthetized mice demonstrate the unique capabilities of the presented architecture in resolving local field potentials from the surface and epidural electrodes.

A 21.3%-Efficiency Clipped-Sinusoid UWB Impulse Radio Transmitter With Simultaneous Inductive Powering and Data Receiving.

An ultra-wide-band impulse-radio (UWB-IR) transmitter (TX) for low-energy biomedical microsystems is presented. High power efficiency is achieved by modulating an LC tank that always resonates in the steady state during transmission. A new clipped-sinusoid scheme is proposed for on-off keying (OOK)-modulation, which is implemented by a voltage clipper circuit with on-chip biasing generation. The TX is designed to provide a high data-rate wireless link within the 3-5 GHz band. The chip was fabricated in 130 nm CMOS technology and fully characterized. State-of-the-art power efficiency of 21.3% was achieved at a data-rate of 230 Mbps and energy consumption of 21pJ/b. A bit-error-rate (BER) of less than 10 -6 was measured at a distance of 1 m without pulse averaging. In addition, simultaneous wireless powering and VCO-based data transmission are supported. A potential extension to a VCO-free all-wireless mode to further reduce the power consumption is also discussed.

Seizure Detection and Prediction by Parallel Memristive Convolutional Neural Networks.

During the past two decades, epileptic seizure detection and prediction algorithms have evolved rapidly. However, despite significant performance improvements, their hardware implementation using conventional technologies, such as Complementary Metal-Oxide-Semiconductor (CMOS), in power and area-constrained settings remains a challenging task; especially when many recording channels are used. In this paper, we propose a novel low-latency parallel Convolutional Neural Network (CNN) architecture that has between 2-2,800x fewer network parameters compared to State-Of-The-Art (SOTA) CNN architectures and achieves 5-fold cross validation accuracy of 99.84% for epileptic seizure detection, and 99.01% and 97.54% for epileptic seizure prediction, when evaluated using the University of Bonn Electroencephalogram (EEG), CHB-MIT and SWEC-ETHZ seizure datasets, respectively. We subsequently implement our network onto analog crossbar arrays comprising Resistive Random-Access Memory (RRAM) devices, and provide a comprehensive benchmark by simulating, laying out, and determining hardware requirements of the CNN component of our system. We parallelize the execution of convolution layer kernels on separate analog crossbars to enable 2 orders of magnitude reduction in latency compared to SOTA hybrid Memristive-CMOS Deep Learning (DL) accelerators. Furthermore, we investigate the effects of non-idealities on our system and investigate Quantization Aware Training (QAT) to mitigate the performance degradation due to low Analog-to-Digital Converter (ADC)/Digital-to-Analog Converter (DAC) resolution. Finally, we propose a stuck weight offsetting methodology to mitigate performance degradation due to stuck [Formula: see text] memristor weights, recovering up to 32% accuracy, without requiring retraining. The CNN component of our platform is estimated to consume approximately 2.791 W of power while occupying an area of 31.255 mm2 in a 22 nm FDSOI CMOS process.

Safety-Optimized Inductive Powering of Implantable Medical Devices: Tutorial and Comprehensive Design Guide.

A tutorial and comprehensive guide are presented for the design of planar spiral inductors with maximum energy delivery in biomedical implants. Rather than maximizing power transfer efficiency (PTE), the ratio of the received power to the square of the magnetic flux density is maximized in this technique. This ensures that the highest power is delivered for a given level of safe electromagnetic radiation, as measured by the specific absorption rate (SAR) in the tissue. By using quasi-static field approximations, the maximum deliverable power under SAR constraints is embedded in a lumped-element model of a 2-coil inductive link, from which planar coil geometries are derived. To compare the proposed methodology with the conventional approach that maximizes PTE, the results of both techniques are compared for three examples of state-of-the-art designs. It is demonstrated that the presented technique increases the maximum deliverable power while operating at a given level of non-ionizing radiation by factors of 8×, 410×, and 560× as compared to the three existing designs, and maintaining moderate link efficiencies of 12%, 23%, and 12%, respectively.

Machine Learning with Imbalanced EEG Datasets using Outlier-based Sampling.

Epilepsy is a neurological disorder which causes seizures in over 65 million people worldwide. Recently developed implantable therapeutic devices aim to prevent symptoms by applying acute electrical stimulation to the seizure-generating brain region in response to activity detected by on-device machine learning hardware. Many training algorithms require an equal number of examples for each target class (e.g. normal activity and seizures), and performance can suffer if this condition is not satisfied. In the case of epilepsy, poor performance can cause seizures to be missed, or stimulation to be applied erroneously. As there is an abundance of normal (interictal) data in clinical EEG recordings, but seizures are rare events (less than 1% of the dataset), the data available for training is severely imbalanced. There are several conventional pre-processing methods used to address imbalanced class learning, such as down-sampling of the majority class and up-sampling of the minority class, but each have performance drawbacks. This paper presents an improved method which involves reducing the majority class down to the most effective interictal outlier samples. Outliers are determined by using Exponentially Decaying Memory Signal Energy (EDMSE) features with Isolation Forests and an ANOVA-based method, which involves comparing a moving feature window to a baseline reference window. Outlier-based sampling is tested with two classifiers (KNN and Logistic Regression) and achieves higher accuracy (∼2% increase) and fewer false positives (∼38% decrease), along with a lower latency (∼3 seconds shorter) compared to conventional training set pre-processing methods.

Asynchronous Event-driven Encoder With Simultaneous Temporal Envelope and Phase Extraction for Cochlear Implants.

Conventional cochlear implants using periodic sampling are power consuming and incapable of capturing the amplitude and phase of the input acoustic signal simultaneously. This paper presents an asynchronous event-driven encoder chip for cochlear implants capable of extracting the temporal fine structure. The chip architecture is based on asynchronous delta modulation (ADM) where the signal peak/trough crossing events are captured and digitized intrinsically, which has the advantages of significantly reduced power consumption, reduced circuit area, and the elimination of dedicated data compression circuitry. An 8-channel prototype chip was fabricated in 0.18 µm 1P6M CMOS process, occupying an area of 0.125 × 1.7 mm2 and has a power consumption of 36.2 µW from a 0.6V supply. A 16-channel stimulation encoding system was built by integrating two test chips, capable of processing the entire audible frequency range from 100 Hz to 10 kHz. Experimental characterization using the human voice is provided to corroborate functionality in the application environment.

Arbitrary-Waveform Electro-Optical Intracranial Neurostimulator With Load-Adaptive High-Voltage Compliance.

A hybrid 16-channel current-mode and the 8-channel optical implantable neurostimulating system is presented. The system generates arbitrary-waveform charge-balanced current-mode electrical pulses with an amplitude ranging from 50 [Formula: see text] to 10 mA. An impedance monitoring feedback loop is employed to automatically adjust the supply voltage, yielding a load-optimized power dissipation. The 8-channel optical stimulator drives an array of LEDs, each with a maximum of 25 mA current amplitude, and reuses the arbitrary-waveform generation function of the electrical stimulator. The LEDs are assembled within a custom-made 4×4 ECoG grid electrode array, enabling precise optical stimulation of neurons with a 300 [Formula: see text] pitch between the LEDs and simultaneous monitoring of the neural response by the ECoG electrode, at different distances of the stimulation site. The hybrid stimulation system is implemented on a mini-PCB, and receives power and stimulation commands inductively through a second board and a coil stacked on top of it. The entire system is sized at 3×2 . 5×1 cm3 and weighs 7 grams. The system efficacy for electrical and optical stimulation is validated in-vivo using separate chronic and acute experiments.

Superresolution Line Scan Image Sensor for Multimodal Microscopy.

A low-cost contact scanning microscope is presented which performs optical imaging of millimeter-scale samples with multiple sensory modalities at a spatial resolution better than the pixel size in both x and y dimensions. The 7.5 mm 3.2 mm 0.35 m CMOS image sensor is comprised of 214 scanning lines of 256 pixels, each line horizontally shifted by 300 nm with respect to the adjacent lines. When scanning in the y dimension, this results in a staircase-like staggered-pixels organization with an effective spatial resolution in the x dimension of less than the pixel size, with a theoretical limit of 300 nm, subject to the light diffraction limit and to photodiode size-dependent spatial aliasing. The height of the resulting pixel "staircases" is capped at 2.5 mm by wrapping the 215th row back to the first row, yielding an approximately 2 mm 2.5 mm instantaneous scanning window size. The spatial resolution in the y dimension is set by the sample scanning rate and the frame rate, subject to the same limitations. Integration of multiple scanning lines naturally lends itself to the inclusion of multiple sensory modalities, with five modalities included as an example: High-resolution (up to 300 nm), fluorescence-sensitive, and triple-orientation light polarization-sensitive pixels. The resulting modified scanning pattern is digitized by on-chip column-parallel 2nd order Delta-Sigma ADCs with ENOB of 9.1 and is reconstructed into a full-resolution image in software. Experimental measurements, where contact-scanning is emulated by the sample image moving on an LCD monitor and projected through a lens, support the validity of the presented concept.

Low-latency VLSI architecture for neural cross-frequency coupling analysis.

Growing evidence suggests that cross-frequency coupling (CFC) is a key mechanism in neuronal computation, communication, and learning in the brain. Abnormal CFC has been implicated in pathological brain states such as epilepsy and Parkinson's disease. A reduction in excessive coupling has been shown in effective neuromodulation treatments, suggesting that CFC may be a useful feedback measure in closed-loop neural stimulation devices. However, processing latency limits the responsiveness of such systems. A VLSI architecture is presented which implements three selectable measures of CFC to enable the application specific trade-off between low-latency and high-accuracy processing. The architecture is demonstrated using in-vitro human neocortical slice recordings, with a latency of 48ms.

Mortality with brainstem seizures from focal 4-aminopyridine-induced recurrent hippocampal seizures.

OBJECTIVE: Sudden unexplained death in epilepsy is the leading cause of death in young adult epilepsy patients, typically occurring during the early postictal period, presumably resulting from brainstem and cardiorespiratory dysfunction. We hypothesized that ictal discharges in the brainstem disrupt the cardiorespiratory network, causing mortality. To study this hypothesis, we chose an animal model comprising focal unilateral hippocampal injection of 4-aminopyridine (4-AP), which produced focal recurrent hippocampal seizures with secondary generalization in awake, behaving rats. METHODS: We studied ictal and interictal intracranial electrographic activity (iEEG) in 23 rats implanted with a custom electrode array into the hippocampus, the contralateral cortex, and brainstem. The hippocampal electrodes contained a cannula to administer the potassium channel blocker and convulsant (4-AP). iEEG was recorded continuously before, during, and after seizures induced by 4-AP infusion into the hippocampus. RESULTS: The control group (n = 5) was monitored for 2-3 months, and the weekly baseline iEEG recordings showed long-term stability. The low-dose group (1 µL 4-AP, 40 mm, n = 5) exhibited local electrographic seizures without spread to the contralateral cerebral cortex or brainstem. The high-dose group (5 µL 4-AP, 40 mm, n = 3) had several hippocampal electrographic seizures, which spread contralaterally and triggered brainstem discharges within 40 min, and were associated with violent motor seizures followed by dyspnea and respiratory arrest, with cortical and hippocampal iEEG flattening. The group that received high-dose 4-AP without brainstem implantation (n = 5) had similar seizure-related respiratory difficulties. Finally, five rats that received high-dose 4-AP without EEG recording also developed violent motor seizures with postictal respiratory arrest. Following visualized respiratory arrest in groups III, IV, and V, manual respiratory resuscitation was successful in five of 13 animals. SIGNIFICANCE: These studies show that hippocampal seizure activity can spread or trigger brainstem epileptiform discharges that may cause mortality, possibly mediated by respiratory network dysfunction.

Closed-Loop Neurostimulators: A Survey and A Seizure-Predicting Design Example for Intractable Epilepsy Treatment.

First, existing commercially available open-loop and closed-loop implantable neurostimulators are reviewed and compared in terms of their targeted application, physical size, system-level features, and performance as a medical device. Next, signal processing algorithms as the primary strength point of the closed-loop neurostimulators are reviewed, and various design and implementation requirements and trade-offs are discussed in details along with quantitative examples. The review results in a set of guidelines for algorithm selection and evaluation. Second, the implementation of an inductively-powered seizure-predicting microsystem for monitoring and treatment of intractable epilepsy is presented. The miniaturized system is comprised of two miniboards and a power receiver coil. The first board hosts a 24-channel neurostimulator system on chip fabricated in a [Formula: see text] CMOS technology and performs neural recording, on-chip digital signal processing, and electrical stimulation. The second board communicates recorded brain signals as well as signal processing results wirelessly. The multilayer flexible coil receives inductively-transmitted power. The system is sized at 2 × 2 × 0.7 [Formula: see text] and weighs 6 g. The approach is validated in the control of chronic seizures in vivo in freely moving rats.

Electronic Sleep Stage Classifiers: A Survey and VLSI Design Methodology.

First, existing sleep stage classifier sensors and algorithms are reviewed and compared in terms of classification accuracy, level of automation, implementation complexity, invasiveness, and targeted application. Next, the implementation of a miniature microsystem for low-latency automatic sleep stage classification in rodents is presented. The classification algorithm uses one EMG (electromyogram) and two EEG (electroencephalogram) signals as inputs in order to detect REM (rapid eye movement) sleep, and is optimized for low complexity and low power consumption. It is implemented in an on-board low-power FPGA connected to a multi-channel neural recording IC, to achieve low-latency (order of 1 ms or less) classification. Off-line experimental results using pre-recorded signals from nine mice show REM detection sensitivity and specificity of 81.69% and 93.86%, respectively, with the maximum latency of 39 [Formula: see text]. The device is designed to be used in a non-disruptive closed-loop REM sleep suppression microsystem, for future studies of the effects of REM sleep deprivation on memory consolidation.

Low-Frequency Noise and Offset Rejection in DC-Coupled Neural Amplifiers: A Review and Digitally-Assisted Design Tutorial.

We review integrated circuits for low-frequency noise and offset rejection as a motivation for the presented digitally-assisted neural amplifier design methodology. Conventional AC-coupled neural amplifiers inherently reject input DC offset but have key limitations in area, linearity, DC drift, and spectral accuracy. Their chopper stabilization reduces low-frequency intrinsic noise at the cost of degraded area, input impedance and design complexity. DC-coupled implementations with digital high-pass filtering yield improved area, linearity, drift, and spectral accuracy and are inherently suitable for simple chopper stabilization. As a design example, a 56-channel 0.13 [Formula: see text] CMOS intracranial EEG interface is presented. DC offset of up to ±50 mV is rejected by a digital low-pass filter and a 16-bit delta-sigma DAC feeding back into the folding node of a folded-cascode LNA with CMRR of 65 dB. A bank of seven column-parallel fully differential SAR ADCs with ENOB of 6.6 are shared among 56 channels resulting in 0.018 [Formula: see text] effective channel area. Compensation-free direct input chopping yields integrated input-referred noise of 4.2 µVrms over the bandwidth of 1 Hz to 1 kHz. The 8.7 [Formula: see text] chip dissipating 1.07 mW has been validated in vivo in online intracranial EEG monitoring in freely moving rats.

Biofouling-Resistant Impedimetric Sensor for Array High-Resolution Extracellular Potassium Monitoring in the Brain.

Extracellular potassium concentration, [K⁺]o, plays a fundamental role in the physiological functions of the brain. Studies investigating changes in [K⁺]o have predominantly relied upon glass capillary electrodes with K⁺-sensitive solution gradients for their measurements. However, such electrodes are unsuitable for taking spatio-temporal measurements and are limited by the surface area of their tips. We illustrate seizures invoked chemically and in optogenetically modified mice using blue light exposure while impedimetrically measuring the response. A sharp decrease of 1-2 mM in [K⁺]o before each spike has shown new physiological events not witnessed previously when measuring extracellular potassium concentrations during seizures in mice. We propose a novel approach that uses multichannel monolayer coated gold microelectrodes for in vivo spatio-temporal measurements of [K⁺]o in a mouse brain as an improvement to the conventional glass capillary electrode.

Low-Radiation Cellular Inductive Powering of Rodent Wireless Brain Interfaces: Methodology and Design Guide.

This paper presents a general methodology of inductive power delivery in wireless chronic rodent electrophysiology applications. The focus is on such systems design considerations under the following key constraints: maximum power delivery under the allowable specific absorption rate (SAR), low cost and spatial scalability. The methodology includes inductive coil design considerations within a low-frequency ferrite-core-free power transfer link which includes a scalable coil-array power transmitter floor and a single-coil implanted or worn power receiver. A specific design example is presented that includes the concept of low-SAR cellular single-transmitter-coil powering through dynamic tracking of a magnet-less receiver spatial location. The transmitter coil instantaneous supply current is monitored using a small number of low-cost electronic components. A drop in its value indicates the proximity of the receiver due to the reflected impedance of the latter. Only the transmitter coil nearest to the receiver is activated. Operating at the low frequency of 1.5 MHz, the inductive powering floor delivers a maximum of 15.9 W below the IEEE C95 SAR limit, which is over three times greater than that in other recently reported designs. The power transfer efficiency of 39% and 13% at the nominal and maximum distances of 8 cm and 11 cm, respectively, is maintained.